{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\lzhen\Dropbox\Trade, Inequality and Government support\Working Paper\PSRM manuscript\Replication materials\LogFile1.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Jan 2022, 13:29:17
{txt}
{com}. 
. version 17.0
{txt}
{com}. clear
{txt}
{com}. ********Trade Openness, Job Sectors, and Social Policy Preferences: Evidence from China********
. **************************Manuscript for PSRM*****************
. **********************Authors: Li Zheng and Ling Zhu****************
. 
. cd "/Users/lzhen/Dropbox/Trade, Inequality and Government support/Working Paper/PSRM manuscript/Replication materials"
{res}C:\Users\lzhen\Dropbox\Trade, Inequality and Government support\Working Paper\PSRM manuscript\Replication materials
{txt}
{com}. 
. use "Data.dta", clear
{txt}
{com}. set scheme s1mono       
{txt}
{com}. graph set window fontface "GillSans"    // turns default graph font into GillSans
{txt}
{com}. 
. //////////////////////////////////////////////////////////
> /////////////   MAIN RESULTS AND FIGURES  ///////////////
> ////////////////////////////////////////////////////////
> 
. ** Trade and Protection in  Table A2 
. 
. gllamm Govresp Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256) link(ologit) adapt
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-2353.3459
{txt}Iteration 1:    log likelihood = {res}-2350.1114
{txt}Iteration 2:    log likelihood = {res}-2348.9452
{txt}Iteration 3:    log likelihood = {res} -2348.792
{txt}Iteration 4:    log likelihood = {res}-2348.7811
{txt}Iteration 5:    log likelihood = {res}-2348.5688
{txt}Iteration 6:    log likelihood = {res}-2348.5037
{txt}Iteration 7:    log likelihood = {res}-2348.5019


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-2348.5019{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-2348.4573{txt}  
Iteration 2:{col 16}log likelihood = {res}-2348.1887{txt}  
Iteration 3:{col 16}log likelihood = {res} -2348.187{txt}  
Iteration 4:{col 16}log likelihood = {res} -2348.187{txt}  
{res} 
{txt}number of level 1 units = {res}1144
{txt}number of level 2 units = {res}24
 
{txt}Condition Number = {res}4306.5451
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-2348.187
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Govresp          {txt}{c |}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0186663{col 30}{space 2} .0054303{col 41}{space 1}    3.44{col 50}{space 3}0.001{col 58}{space 4} .0080231{col 71}{space 3} .0293094
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3292575{col 30}{space 2} .1295149{col 41}{space 1}   -2.54{col 50}{space 3}0.011{col 58}{space 4}-.5831021{col 71}{space 3} -.075413
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5262243{col 30}{space 2} .2157235{col 41}{space 1}    2.44{col 50}{space 3}0.015{col 58}{space 4} .1034139{col 71}{space 3} .9490347
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} .0004552{col 30}{space 2} .0058988{col 41}{space 1}    0.08{col 50}{space 3}0.938{col 58}{space 4}-.0111063{col 71}{space 3} .0120167
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0532465{col 30}{space 2} .0129888{col 41}{space 1}   -4.10{col 50}{space 3}0.000{col 58}{space 4}-.0787041{col 71}{space 3}-.0277888
{txt}{space 10}Income {c |}{col 18}{res}{space 2} -.041323{col 30}{space 2} .0312125{col 41}{space 1}   -1.32{col 50}{space 3}0.186{col 58}{space 4}-.1024983{col 71}{space 3} .0198523
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0642827{col 30}{space 2} .1209035{col 41}{space 1}   -0.53{col 50}{space 3}0.595{col 58}{space 4}-.3012492{col 71}{space 3} .1726837
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1091159{col 30}{space 2} .1059689{col 41}{space 1}   -1.03{col 50}{space 3}0.303{col 58}{space 4}-.3168112{col 71}{space 3} .0985794
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0022722{col 30}{space 2} .0304695{col 41}{space 1}    0.07{col 50}{space 3}0.941{col 58}{space 4}-.0574469{col 71}{space 3} .0619912
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0380866{col 30}{space 2} .0250869{col 41}{space 1}   -1.52{col 50}{space 3}0.129{col 58}{space 4}-.0872559{col 71}{space 3} .0110828
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0018508{col 30}{space 2} .0047687{col 41}{space 1}    0.39{col 50}{space 3}0.698{col 58}{space 4}-.0074958{col 71}{space 3} .0111973
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3928658{col 30}{space 2}  .024277{col 41}{space 1}   16.18{col 50}{space 3}0.000{col 58}{space 4} .3452837{col 71}{space 3} .4404478
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.726238{col 30}{space 2} 1.119286{col 41}{space 1}   -1.54{col 50}{space 3}0.123{col 58}{space 4}-3.919998{col 71}{space 3}  .467522
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.6194975{col 30}{space 2} 1.113523{col 41}{space 1}   -0.56{col 50}{space 3}0.578{col 58}{space 4}-2.801963{col 71}{space 3} 1.562968
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .3143071{col 30}{space 2} 1.112567{col 41}{space 1}    0.28{col 50}{space 3}0.778{col 58}{space 4}-1.866285{col 71}{space 3} 2.494899
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .9908983{col 30}{space 2} 1.112868{col 41}{space 1}    0.89{col 50}{space 3}0.373{col 58}{space 4}-1.190284{col 71}{space 3}  3.17208
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}  1.53394{col 30}{space 2} 1.113461{col 41}{space 1}    1.38{col 50}{space 3}0.168{col 58}{space 4} -.648403{col 71}{space 3} 3.716283
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.033067{col 30}{space 2} 1.114052{col 41}{space 1}    1.82{col 50}{space 3}0.068{col 58}{space 4}-.1504358{col 71}{space 3} 4.216569
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.665761{col 30}{space 2} 1.115042{col 41}{space 1}    2.39{col 50}{space 3}0.017{col 58}{space 4} .4803177{col 71}{space 3} 4.851204
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 3.781747{col 30}{space 2} 1.117392{col 41}{space 1}    3.38{col 50}{space 3}0.001{col 58}{space 4}   1.5917{col 71}{space 3} 5.971795
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 4.439942{col 30}{space 2} 1.119319{col 41}{space 1}    3.97{col 50}{space 3}0.000{col 58}{space 4} 2.246117{col 71}{space 3} 6.633766
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.01887295 (.02803661)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. gllamm Unemploy Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256) link(ologit) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-1840.6192
{txt}Iteration 1:    log likelihood = {res}-1838.8531
{txt}Iteration 2:    log likelihood = {res}-1838.7218
{txt}Iteration 3:    log likelihood = {res} -1838.716
{txt}Iteration 4:    log likelihood = {res}-1838.7071
{txt}Iteration 5:    log likelihood = {res}  -1838.69
{txt}Iteration 6:    log likelihood = {res}-1838.6888


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-1838.6888{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-1838.6888{txt}  
Iteration 2:{col 16}log likelihood = {res}-1838.6312{txt}  
Iteration 3:{col 16}log likelihood = {res}-1838.6309{txt}  
Iteration 4:{col 16}log likelihood = {res}-1838.6309{txt}  
{res} 
{txt}number of level 1 units = {res}1071
{txt}number of level 2 units = {res}23
 
{txt}Condition Number = {res}4006.7992
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-1838.6309
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Unemploy         {txt}{c |}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0223417{col 30}{space 2} .0104339{col 41}{space 1}    2.14{col 50}{space 3}0.032{col 58}{space 4} .0018916{col 71}{space 3} .0427919
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.1986315{col 30}{space 2} .1400406{col 41}{space 1}   -1.42{col 50}{space 3}0.156{col 58}{space 4}-.4731061{col 71}{space 3}  .075843
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5459761{col 30}{space 2} .3959307{col 41}{space 1}    1.38{col 50}{space 3}0.168{col 58}{space 4}-.2300339{col 71}{space 3} 1.321986
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}  -.01725{col 30}{space 2} .0114507{col 41}{space 1}   -1.51{col 50}{space 3}0.132{col 58}{space 4} -.039693{col 71}{space 3}  .005193
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0264729{col 30}{space 2} .0249156{col 41}{space 1}   -1.06{col 50}{space 3}0.288{col 58}{space 4}-.0753067{col 71}{space 3} .0223609
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0754214{col 30}{space 2} .0343211{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4}-.1426895{col 71}{space 3}-.0081533
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0661578{col 30}{space 2}  .131148{col 41}{space 1}   -0.50{col 50}{space 3}0.614{col 58}{space 4}-.3232033{col 71}{space 3} .1908876
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1986043{col 30}{space 2} .1135385{col 41}{space 1}   -1.75{col 50}{space 3}0.080{col 58}{space 4}-.4211357{col 71}{space 3}  .023927
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0437663{col 30}{space 2} .0330105{col 41}{space 1}   -1.33{col 50}{space 3}0.185{col 58}{space 4}-.1084657{col 71}{space 3}  .020933
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}   .02977{col 30}{space 2} .0256517{col 41}{space 1}    1.16{col 50}{space 3}0.246{col 58}{space 4}-.0205064{col 71}{space 3} .0800463
{txt}{space 13}Age {c |}{col 18}{res}{space 2} -.004378{col 30}{space 2} .0050706{col 41}{space 1}   -0.86{col 50}{space 3}0.388{col 58}{space 4}-.0143162{col 71}{space 3} .0055603
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0775275{col 30}{space 2} .0219855{col 41}{space 1}    3.53{col 50}{space 3}0.000{col 58}{space 4} .0344367{col 71}{space 3} .1206183
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-3.519512{col 30}{space 2} 1.993648{col 41}{space 1}   -1.77{col 50}{space 3}0.078{col 58}{space 4} -7.42699{col 71}{space 3} .3879664
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.898219{col 30}{space 2} 1.988302{col 41}{space 1}   -1.46{col 50}{space 3}0.145{col 58}{space 4} -6.79522{col 71}{space 3} .9987817
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.279682{col 30}{space 2}  1.98582{col 41}{space 1}   -1.15{col 50}{space 3}0.251{col 58}{space 4}-6.171817{col 71}{space 3} 1.612454
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.008061{col 30}{space 2} 1.985158{col 41}{space 1}   -1.01{col 50}{space 3}0.312{col 58}{space 4}-5.898899{col 71}{space 3} 1.882778
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.693871{col 30}{space 2} 1.984427{col 41}{space 1}   -0.85{col 50}{space 3}0.393{col 58}{space 4}-5.583276{col 71}{space 3} 2.195534
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.304389{col 30}{space 2} 1.983538{col 41}{space 1}   -0.66{col 50}{space 3}0.511{col 58}{space 4}-5.192053{col 71}{space 3} 2.583274
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.6846906{col 30}{space 2} 1.982654{col 41}{space 1}   -0.35{col 50}{space 3}0.730{col 58}{space 4}-4.570622{col 71}{space 3}  3.20124
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .4999032{col 30}{space 2} 1.982037{col 41}{space 1}    0.25{col 50}{space 3}0.801{col 58}{space 4}-3.384817{col 71}{space 3} 4.384624
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.591301{col 30}{space 2} 1.982258{col 41}{space 1}    0.80{col 50}{space 3}0.422{col 58}{space 4}-2.293854{col 71}{space 3} 5.476456
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.2172654 (.09041312)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. ** Exports / Imports and Protection in Table A3
. 
. gllamm Govresp Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256)link(ologit) adapt
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res} -2353.272
{txt}Iteration 1:    log likelihood = {res}-2349.1652
{txt}Iteration 2:    log likelihood = {res}-2348.2887
{txt}Iteration 3:    log likelihood = {res}-2348.2788
{txt}Iteration 4:    log likelihood = {res}-2348.2788


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-2348.2788{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-2348.2788{txt}  
Iteration 2:{col 16}log likelihood = {res}-2348.1179{txt}  
Iteration 3:{col 16}log likelihood = {res}-2348.0787{txt}  
Iteration 4:{col 16}log likelihood = {res}-2348.0787{txt}  
{res} 
{txt}number of level 1 units = {res}1144
{txt}number of level 2 units = {res}24
 
{txt}Condition Number = {res}4081.02
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-2348.0787
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Govresp          {txt}{c |}
{space 7}Exportlib {c |}{col 18}{res}{space 2} .0205138{col 30}{space 2} .0067898{col 41}{space 1}    3.02{col 50}{space 3}0.003{col 58}{space 4} .0072062{col 71}{space 3} .0338215
{txt}{space 7}Importlib {c |}{col 18}{res}{space 2} .0163025{col 30}{space 2} .0075323{col 41}{space 1}    2.16{col 50}{space 3}0.030{col 58}{space 4} .0015396{col 71}{space 3} .0310655
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3273641{col 30}{space 2} .1296264{col 41}{space 1}   -2.53{col 50}{space 3}0.012{col 58}{space 4}-.5814272{col 71}{space 3}-.0733009
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5125444{col 30}{space 2} .2193352{col 41}{space 1}    2.34{col 50}{space 3}0.019{col 58}{space 4} .0826553{col 71}{space 3} .9424336
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0003455{col 30}{space 2} .0061758{col 41}{space 1}   -0.06{col 50}{space 3}0.955{col 58}{space 4}-.0124499{col 71}{space 3} .0117589
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0482783{col 30}{space 2} .0171079{col 41}{space 1}   -2.82{col 50}{space 3}0.005{col 58}{space 4}-.0818092{col 71}{space 3}-.0147474
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0409078{col 30}{space 2} .0312251{col 41}{space 1}   -1.31{col 50}{space 3}0.190{col 58}{space 4}-.1021079{col 71}{space 3} .0202923
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0670568{col 30}{space 2} .1210875{col 41}{space 1}   -0.55{col 50}{space 3}0.580{col 58}{space 4} -.304384{col 71}{space 3} .1702703
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1094443{col 30}{space 2} .1059839{col 41}{space 1}   -1.03{col 50}{space 3}0.302{col 58}{space 4} -.317169{col 71}{space 3} .0982803
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0014083{col 30}{space 2} .0305302{col 41}{space 1}    0.05{col 50}{space 3}0.963{col 58}{space 4}-.0584297{col 71}{space 3} .0612463
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0387599{col 30}{space 2}  .025114{col 41}{space 1}   -1.54{col 50}{space 3}0.123{col 58}{space 4}-.0879825{col 71}{space 3} .0104628
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0018987{col 30}{space 2} .0047703{col 41}{space 1}    0.40{col 50}{space 3}0.691{col 58}{space 4} -.007451{col 71}{space 3} .0112483
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3930792{col 30}{space 2} .0242996{col 41}{space 1}   16.18{col 50}{space 3}0.000{col 58}{space 4} .3454529{col 71}{space 3} .4407056
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.603445{col 30}{space 2} 1.161483{col 41}{space 1}   -1.38{col 50}{space 3}0.167{col 58}{space 4} -3.87991{col 71}{space 3}  .673021
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.4963557{col 30}{space 2}  1.15619{col 41}{space 1}   -0.43{col 50}{space 3}0.668{col 58}{space 4}-2.762446{col 71}{space 3} 1.769735
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .4386651{col 30}{space 2} 1.155919{col 41}{space 1}    0.38{col 50}{space 3}0.704{col 58}{space 4}-1.826895{col 71}{space 3} 2.704225
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.116238{col 30}{space 2} 1.156741{col 41}{space 1}    0.96{col 50}{space 3}0.335{col 58}{space 4}-1.150933{col 71}{space 3}  3.38341
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.659702{col 30}{space 2} 1.157584{col 41}{space 1}    1.43{col 50}{space 3}0.152{col 58}{space 4}-.6091204{col 71}{space 3} 3.928525
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.158924{col 30}{space 2} 1.158258{col 41}{space 1}    1.86{col 50}{space 3}0.062{col 58}{space 4}-.1112204{col 71}{space 3} 4.429069
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}   2.7913{col 30}{space 2} 1.159128{col 41}{space 1}    2.41{col 50}{space 3}0.016{col 58}{space 4}   .51945{col 71}{space 3}  5.06315
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 3.906812{col 30}{space 2} 1.161264{col 41}{space 1}    3.36{col 50}{space 3}0.001{col 58}{space 4} 1.630777{col 71}{space 3} 6.182847
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}  4.56497{col 30}{space 2} 1.163108{col 41}{space 1}    3.92{col 50}{space 3}0.000{col 58}{space 4}  2.28532{col 71}{space 3} 6.844619
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.02033676 (.02870841)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. gllamm Unemploy Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V25)link(ologit) adapt
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-1839.3597
{txt}Iteration 1:    log likelihood = {res}-1838.0493
{txt}Iteration 2:    log likelihood = {res}-1837.5675
{txt}Iteration 3:    log likelihood = {res}-1837.5505
{txt}Iteration 4:    log likelihood = {res}-1837.5007
{txt}Iteration 5:    log likelihood = {res}-1837.4503
{txt}Iteration 6:    log likelihood = {res}-1837.3935
{txt}Iteration 7:    log likelihood = {res}-1837.3906
{txt}Iteration 8:    log likelihood = {res}-1837.3901


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-1837.3901{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-1837.3901{txt}  
Iteration 2:{col 16}log likelihood = {res}-1837.3369{txt}  
Iteration 3:{col 16}log likelihood = {res}-1837.3366{txt}  
Iteration 4:{col 16}log likelihood = {res}-1837.3366{txt}  
{res} 
{txt}number of level 1 units = {res}1071
{txt}number of level 2 units = {res}23
 
{txt}Condition Number = {res}3866.0897
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-1837.3366
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Unemploy         {txt}{c |}
{space 7}Exportlib {c |}{col 18}{res}{space 2} .0096551{col 30}{space 2} .0124497{col 41}{space 1}    0.78{col 50}{space 3}0.438{col 58}{space 4}-.0147459{col 71}{space 3} .0340561
{txt}{space 7}Importlib {c |}{col 18}{res}{space 2} .0363827{col 30}{space 2} .0128453{col 41}{space 1}    2.83{col 50}{space 3}0.005{col 58}{space 4} .0112063{col 71}{space 3} .0615591
{txt}{space 9}Private {c |}{col 18}{res}{space 2} -.204682{col 30}{space 2}   .14002{col 41}{space 1}   -1.46{col 50}{space 3}0.144{col 58}{space 4}-.4791162{col 71}{space 3} .0697523
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .6543912{col 30}{space 2} .3780195{col 41}{space 1}    1.73{col 50}{space 3}0.083{col 58}{space 4}-.0865135{col 71}{space 3} 1.395296
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0108261{col 30}{space 2} .0113782{col 41}{space 1}   -0.95{col 50}{space 3}0.341{col 58}{space 4}-.0331269{col 71}{space 3} .0114748
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0553358{col 30}{space 2} .0292493{col 41}{space 1}   -1.89{col 50}{space 3}0.059{col 58}{space 4}-.1126634{col 71}{space 3} .0019919
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0775177{col 30}{space 2} .0343372{col 41}{space 1}   -2.26{col 50}{space 3}0.024{col 58}{space 4}-.1448174{col 71}{space 3}-.0102181
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0597258{col 30}{space 2} .1311627{col 41}{space 1}   -0.46{col 50}{space 3}0.649{col 58}{space 4}-.3167999{col 71}{space 3} .1973484
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1996387{col 30}{space 2}  .113507{col 41}{space 1}   -1.76{col 50}{space 3}0.079{col 58}{space 4}-.4221084{col 71}{space 3} .0228309
{txt}{space 7}Education {c |}{col 18}{res}{space 2} -.042497{col 30}{space 2} .0329916{col 41}{space 1}   -1.29{col 50}{space 3}0.198{col 58}{space 4}-.1071593{col 71}{space 3} .0221652
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0303817{col 30}{space 2} .0256528{col 41}{space 1}    1.18{col 50}{space 3}0.236{col 58}{space 4}-.0198968{col 71}{space 3} .0806603
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0045915{col 30}{space 2} .0050717{col 41}{space 1}   -0.91{col 50}{space 3}0.365{col 58}{space 4}-.0145319{col 71}{space 3} .0053489
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0768745{col 30}{space 2} .0219524{col 41}{space 1}    3.50{col 50}{space 3}0.000{col 58}{space 4} .0338487{col 71}{space 3} .1199004
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} -4.14035{col 30}{space 2} 1.936817{col 41}{space 1}   -2.14{col 50}{space 3}0.033{col 58}{space 4}-7.936441{col 71}{space 3}-.3442589
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-3.518717{col 30}{space 2} 1.931199{col 41}{space 1}   -1.82{col 50}{space 3}0.068{col 58}{space 4}-7.303797{col 71}{space 3}  .266363
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.899393{col 30}{space 2} 1.928418{col 41}{space 1}   -1.50{col 50}{space 3}0.133{col 58}{space 4}-6.679023{col 71}{space 3} .8802372
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.627499{col 30}{space 2}  1.92764{col 41}{space 1}   -1.36{col 50}{space 3}0.173{col 58}{space 4}-6.405603{col 71}{space 3} 1.150606
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.313179{col 30}{space 2} 1.926804{col 41}{space 1}   -1.20{col 50}{space 3}0.230{col 58}{space 4}-6.089645{col 71}{space 3} 1.463287
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.923462{col 30}{space 2} 1.925752{col 41}{space 1}   -1.00{col 50}{space 3}0.318{col 58}{space 4}-5.697867{col 71}{space 3} 1.850943
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.303619{col 30}{space 2} 1.924627{col 41}{space 1}   -0.68{col 50}{space 3}0.498{col 58}{space 4}-5.075818{col 71}{space 3}  2.46858
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.1189635{col 30}{space 2} 1.923592{col 41}{space 1}   -0.06{col 50}{space 3}0.951{col 58}{space 4}-3.889134{col 71}{space 3} 3.651207
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .9711064{col 30}{space 2} 1.923639{col 41}{space 1}    0.50{col 50}{space 3}0.614{col 58}{space 4}-2.799157{col 71}{space 3}  4.74137
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V25{txt})
{res} 
{txt}    var(1): {res}.18125102 (.0802263)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. 
. ** Trade, Job Sectors, and Social Protection in Table A4
. 
. gen interaction=Tradelib*Private
{txt}(875 missing values generated)

{com}. 
. ologit Govresp Tradelib Private interaction unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2534.9073}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2354.0718}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2347.4893}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2347.4688}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2347.4688}  
{res}
{txt}{col 1}Ordered logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:1,144}
{txt}{col 56}{lalign 13:Wald chi2({res:13})}{col 69} = {res}{ralign 7:1151.93}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2347.4688}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0739}

{txt}{ralign 82:(Std. err. adjusted for {res:24} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0156927{col 30}{space 2} .0058657{col 41}{space 1}    2.68{col 50}{space 3}0.007{col 58}{space 4} .0041962{col 71}{space 3} .0271891
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.4758718{col 30}{space 2} .1889935{col 41}{space 1}   -2.52{col 50}{space 3}0.012{col 58}{space 4}-.8462922{col 71}{space 3}-.1054514
{txt}{space 5}interaction {c |}{col 18}{res}{space 2} .0045588{col 30}{space 2} .0022926{col 41}{space 1}    1.99{col 50}{space 3}0.047{col 58}{space 4} .0000654{col 71}{space 3} .0090522
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5107886{col 30}{space 2} .2251805{col 41}{space 1}    2.27{col 50}{space 3}0.023{col 58}{space 4} .0694429{col 71}{space 3} .9521342
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} .0000258{col 30}{space 2} .0027736{col 41}{space 1}    0.01{col 50}{space 3}0.993{col 58}{space 4}-.0054103{col 71}{space 3}  .005462
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0553638{col 30}{space 2} .0144077{col 41}{space 1}   -3.84{col 50}{space 3}0.000{col 58}{space 4}-.0836023{col 71}{space 3}-.0271253
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0469626{col 30}{space 2} .0351949{col 41}{space 1}   -1.33{col 50}{space 3}0.182{col 58}{space 4}-.1159433{col 71}{space 3} .0220181
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0670704{col 30}{space 2} .1261565{col 41}{space 1}   -0.53{col 50}{space 3}0.595{col 58}{space 4}-.3143325{col 71}{space 3} .1801918
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1047862{col 30}{space 2} .0898569{col 41}{space 1}   -1.17{col 50}{space 3}0.244{col 58}{space 4}-.2809024{col 71}{space 3} .0713301
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0022588{col 30}{space 2} .0424335{col 41}{space 1}    0.05{col 50}{space 3}0.958{col 58}{space 4}-.0809094{col 71}{space 3}  .085427
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0346858{col 30}{space 2} .0253732{col 41}{space 1}   -1.37{col 50}{space 3}0.172{col 58}{space 4}-.0844164{col 71}{space 3} .0150449
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0015026{col 30}{space 2} .0047565{col 41}{space 1}    0.32{col 50}{space 3}0.752{col 58}{space 4}-.0078199{col 71}{space 3} .0108251
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2}  .397041{col 30}{space 2} .0291546{col 41}{space 1}   13.62{col 50}{space 3}0.000{col 58}{space 4} .3398991{col 71}{space 3} .4541829
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-1.977216{col 30}{space 2}  1.18101{col 58}{space 4}-4.291953{col 71}{space 3} .3375216
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2} -.873134{col 30}{space 2} 1.267133{col 58}{space 4}-3.356669{col 71}{space 3} 1.610401
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}   .05813{col 30}{space 2} 1.238235{col 58}{space 4}-2.368767{col 71}{space 3} 2.485027
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2} .7329183{col 30}{space 2} 1.200779{col 58}{space 4}-1.620565{col 71}{space 3} 3.086401
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2} 1.273756{col 30}{space 2}  1.17367{col 58}{space 4}-1.026594{col 71}{space 3} 3.574106
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2} 1.771161{col 30}{space 2} 1.189323{col 58}{space 4}-.5598699{col 71}{space 3} 4.102191
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} 2.401615{col 30}{space 2} 1.178378{col 58}{space 4} .0920364{col 71}{space 3} 4.711193
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2}  3.51696{col 30}{space 2} 1.123904{col 58}{space 4} 1.314149{col 71}{space 3} 5.719771
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 4.177701{col 30}{space 2} 1.096497{col 58}{space 4} 2.028607{col 71}{space 3} 6.326795
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit Unemploy Tradelib Private interaction unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1882.9868}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1856.589}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1856.5121}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1856.5121}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,071}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:52.61}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1856.5121}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0141}

{txt}{ralign 82:(Std. err. adjusted for {res:23} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0215982{col 30}{space 2} .0096545{col 41}{space 1}    2.24{col 50}{space 3}0.025{col 58}{space 4} .0026757{col 71}{space 3} .0405207
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.2429485{col 30}{space 2} .1809058{col 41}{space 1}   -1.34{col 50}{space 3}0.179{col 58}{space 4}-.5975173{col 71}{space 3} .1116204
{txt}{space 5}interaction {c |}{col 18}{res}{space 2}  .003716{col 30}{space 2} .0024014{col 41}{space 1}    1.55{col 50}{space 3}0.122{col 58}{space 4}-.0009906{col 71}{space 3} .0084226
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .7991197{col 30}{space 2} .5049807{col 41}{space 1}    1.58{col 50}{space 3}0.114{col 58}{space 4}-.1906243{col 71}{space 3} 1.788864
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0160134{col 30}{space 2} .0121242{col 41}{space 1}   -1.32{col 50}{space 3}0.187{col 58}{space 4}-.0397765{col 71}{space 3} .0077497
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0192219{col 30}{space 2} .0150135{col 41}{space 1}   -1.28{col 50}{space 3}0.200{col 58}{space 4}-.0486478{col 71}{space 3} .0102039
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.1009162{col 30}{space 2} .0575882{col 41}{space 1}   -1.75{col 50}{space 3}0.080{col 58}{space 4} -.213787{col 71}{space 3} .0119546
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0714441{col 30}{space 2}  .130046{col 41}{space 1}   -0.55{col 50}{space 3}0.583{col 58}{space 4}-.3263295{col 71}{space 3} .1834413
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.2072969{col 30}{space 2} .1295008{col 41}{space 1}   -1.60{col 50}{space 3}0.109{col 58}{space 4}-.4611139{col 71}{space 3} .0465201
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0323415{col 30}{space 2} .0421082{col 41}{space 1}   -0.77{col 50}{space 3}0.442{col 58}{space 4} -.114872{col 71}{space 3}  .050189
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0252735{col 30}{space 2} .0267086{col 41}{space 1}    0.95{col 50}{space 3}0.344{col 58}{space 4}-.0270744{col 71}{space 3} .0776213
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0041836{col 30}{space 2} .0054701{col 41}{space 1}   -0.76{col 50}{space 3}0.444{col 58}{space 4}-.0149048{col 71}{space 3} .0065375
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0751982{col 30}{space 2} .0559238{col 41}{space 1}    1.34{col 50}{space 3}0.179{col 58}{space 4}-.0344105{col 71}{space 3} .1848069
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-2.241781{col 30}{space 2} 2.260821{col 58}{space 4}-6.672909{col 71}{space 3} 2.189348
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-1.630911{col 30}{space 2} 2.329842{col 58}{space 4}-6.197317{col 71}{space 3} 2.935495
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}-1.031797{col 30}{space 2} 2.341948{col 58}{space 4}-5.621931{col 71}{space 3} 3.558337
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2}-.7689672{col 30}{space 2} 2.315667{col 58}{space 4}-5.307591{col 71}{space 3} 3.769657
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2}-.4641717{col 30}{space 2} 2.292788{col 58}{space 4}-4.957954{col 71}{space 3} 4.029611
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2}-.0902005{col 30}{space 2} 2.262943{col 58}{space 4}-4.525486{col 71}{space 3} 4.345085
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} .5008246{col 30}{space 2} 2.241824{col 58}{space 4} -3.89307{col 71}{space 3} 4.894719
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 1.632444{col 30}{space 2} 2.224092{col 58}{space 4}-2.726696{col 71}{space 3} 5.991584
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 2.681979{col 30}{space 2} 2.230236{col 58}{space 4}-1.689204{col 71}{space 3} 7.053162
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. **Figure 1, Table A2
. 
. quietly gllamm Govresp Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256) link(ologit) adapt
{txt}
{com}. estimate store g
{txt}
{com}. quietly gllamm Unemploy Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256) link(ologit) adapt 
{txt}
{com}. estimate store u
{txt}
{com}. 
. set scheme s2mono
{txt}
{com}. 
. coefplot (g, label (Government)) (u, label (Unemploy)), drop (_cons Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP) eform xscale(log) xline (1) ciopts(recast(rcap)) graphregion(color(white)) bgcolor(white) saving (Figure1a.gph)
{res}{txt}file {bf:Figure1a.gph} saved

{com}. 
. coefplot (g, label (Government)) (u, label (Unemploy)), drop (_cons Tradelib unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP) eform xscale(log) xline (1) ciopts(recast(rcap)) graphregion(color(white)) bgcolor(white) saving (Figure1b.gph)
{res}{txt}file {bf:Figure1b.gph} saved

{com}. 
. gr combine Figure1a.gph  Figure1b.gph 
{res}{txt}
{com}. 
. **Export and Import: Figure 2, Table A3 
. 
. quietly gllamm Govresp Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256) link(ologit) adapt
{txt}
{com}. estimate store g1
{txt}
{com}. 
. quietly gllamm Unemploy Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256) link(ologit) adapt
{txt}
{com}. estimate store u1
{txt}
{com}. 
. set scheme s2mono
{txt}
{com}. coefplot (g1, label (Government)) (u1, label (Unemploy)), drop (_cons Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP) eform xscale(log) xline (1) ciopts(recast(rcap)) graphregion(color(white)) bgcolor(white) saving (Figure2a.gph)
{res}{txt}file {bf:Figure2a.gph} saved

{com}. 
. coefplot (g1, label (Government)) (u1, label (Unemploy)), drop (_cons Exportlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP) eform xscale(log) xline (1) ciopts(recast(rcap)) graphregion(color(white)) bgcolor(white) saving (Figure2b.gph)
{res}{txt}file {bf:Figure2b.gph} saved

{com}. 
. gr combine Figure2a.gph  Figure2b.gph 
{res}{txt}
{com}. 
. 
. ** Interflex for Figure 3, Table A4
. 
. interflex Govresp Private Tradelib unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, vce(boot) reps(1500) ylab (Gov Role) dlab(Private Employee) xlab(Trade Openness-Province) type(linear) saving(Figure3)
{txt}Bootstrapping...
{res}{txt}
{com}. 
. ** Interflex for Figure 4, Table A4
. 
. interflex Govresp Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, vce(boot) reps(1500) ylab (Gov Role) dlab(Trade Openness-Province) xlab(Private Employee) type(linear) saving(Figure4)
{txt}Bootstrapping...
{res}{txt}
{com}. 
. **DV visuals in Figure A1 of the statistical appendix
. 
. set scheme s2mono
{txt}
{com}. hist Govresp, percent normal addlabels addlabopts(yvarformat(%4.2f)) ylabel(0(10)20, grid) xlabel(1(1)10) graphregion(color(white)) bgcolor(white) scheme(s2mono) saving(FigureA1a)
{txt}(bin={res}33{txt}, start={res}1{txt}, width={res}.27272727{txt})
{res}{txt}file {bf:FigureA1a.gph} saved

{com}. hist Unemploy, percent normal addlabels addlabopts(yvarformat(%4.2f)) ylabel(0(10)30, grid) xlabel(1(1)10) graphregion(color(white)) bgcolor(white) scheme(s2mono) saving (FigureA1b)
{txt}(bin={res}32{txt}, start={res}1{txt}, width={res}.28125{txt})
{res}{txt}file {bf:FigureA1b.gph} saved

{com}. 
. gr combine FigureA1a.gph  FigureA1b.gph 
{res}{txt}
{com}. 
. 
. **Trade visual in Figure A2 of the statistical appendix is provided separately by using R codes
. 
. 
. 
. //////////////////////////////////////////////////////////
> /////////////   APPENDIX  ///////////////////////////////
> ////////////////////////////////////////////////////////
> 
. ** Summary of statistics, Table A1
. 
. sum Govresp Unemploy Tradelib Exportlib Importlib Private unemployment Income FDIlib Tertiaryindustry employed Age Education Skill Female EqualityP

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}Govresp {c |}{res}      2,123    6.355629    2.653444          1         10
{txt}{space 4}Unemploy {c |}{res}      1,968    8.189024    1.999451          1         10
{txt}{space 4}Tradelib {c |}{res}      2,300    34.08835     38.6807   3.857133   144.0293
{txt}{space 3}Exportlib {c |}{res}      2,300    17.85904    19.18483   2.428507   64.63609
{txt}{space 3}Importlib {c |}{res}      2,300    16.22931    23.32766   1.428626   122.9839
{txt}{hline 13}{c +}{hline 57}
{space 5}Private {c |}{res}      1,425     .682807    .4655467          0          1
{txt}unemployment {c |}{res}      2,300     3.46387    .5446675        1.4        4.2
{txt}{space 6}Income {c |}{res}      2,055    4.416058    1.852821          1         10
{txt}{space 6}FDIlib {c |}{res}      2,300    28.57968    26.73014   7.090716   129.3784
{txt}Tertiaryin~y {c |}{res}      2,300    40.60613     8.17011       30.9       76.5
{txt}{hline 13}{c +}{hline 57}
{space 4}employed {c |}{res}      2,300    .5013043     .500107          0          1
{txt}{space 9}Age {c |}{res}      2,300    43.91826    14.94669         18         75
{txt}{space 3}Education {c |}{res}      2,300    5.337391    2.366412          1          9
{txt}{space 7}Skill {c |}{res}      1,416     5.30226    2.585722          1         10
{txt}{space 6}Female {c |}{res}      2,300    .5104348    .4999998          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}EqualityP {c |}{res}      2,130    6.548357    2.742214          1         10
{txt}
{com}. 
. 
. **Response rate, Table A5, Table A6
. 
. recode Govresp (1/10=1) (.=0), generate (gov_rate)
{txt}(2213 differences between {bf:Govresp} and {bf:gov_rate})

{com}. recode Unemploy (1/10=1) (.=0), generate (un_rate)
{txt}(2268 differences between {bf:Unemploy} and {bf:un_rate})

{com}. 
. label variable gov_rate "Govt Response" 
{txt}
{com}. label variable un_rate "Unemploy Response"
{txt}
{com}. 
. logit gov_rate Tradelib Private unemployment Education Female Income employed Age EqualityP, robust 

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-32.312795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-30.599808}  
Iteration 2:{space 3}log pseudolikelihood = {res:-29.375303}  
Iteration 3:{space 3}log pseudolikelihood = {res:-29.373125}  
Iteration 4:{space 3}log pseudolikelihood = {res:-29.373124}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,181}
{txt}{col 57}{lalign 13:Wald chi2({res:9})}{col 70} = {res}{ralign 6:144.78}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-29.373124}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0910}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    gov_rate{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Tradelib {c |}{col 14}{res}{space 2}-.0132939{col 26}{space 2} .0103573{col 37}{space 1}   -1.28{col 46}{space 3}0.199{col 54}{space 4}-.0335938{col 67}{space 3}  .007006
{txt}{space 5}Private {c |}{col 14}{res}{space 2} .2728161{col 26}{space 2} .9403356{col 37}{space 1}    0.29{col 46}{space 3}0.772{col 54}{space 4}-1.570208{col 67}{space 3}  2.11584
{txt}unemployment {c |}{col 14}{res}{space 2}-1.207799{col 26}{space 2}    1.159{col 37}{space 1}   -1.04{col 46}{space 3}0.297{col 54}{space 4}-3.479397{col 67}{space 3} 1.063798
{txt}{space 3}Education {c |}{col 14}{res}{space 2} .0010334{col 26}{space 2} .0878303{col 37}{space 1}    0.01{col 46}{space 3}0.991{col 54}{space 4}-.1711108{col 67}{space 3} .1731775
{txt}{space 6}Female {c |}{col 14}{res}{space 2}-.4047503{col 26}{space 2}  .809821{col 37}{space 1}   -0.50{col 46}{space 3}0.617{col 54}{space 4} -1.99197{col 67}{space 3}  1.18247
{txt}{space 6}Income {c |}{col 14}{res}{space 2} .0302721{col 26}{space 2} .2009254{col 37}{space 1}    0.15{col 46}{space 3}0.880{col 54}{space 4}-.3635345{col 67}{space 3} .4240787
{txt}{space 4}employed {c |}{col 14}{res}{space 2} 1.734706{col 26}{space 2} 1.135635{col 37}{space 1}    1.53{col 46}{space 3}0.127{col 54}{space 4}-.4910972{col 67}{space 3}  3.96051
{txt}{space 9}Age {c |}{col 14}{res}{space 2}-.0036044{col 26}{space 2} .0245165{col 37}{space 1}   -0.15{col 46}{space 3}0.883{col 54}{space 4}-.0516559{col 67}{space 3} .0444471
{txt}{space 3}EqualityP {c |}{col 14}{res}{space 2} .1491707{col 26}{space 2} .0926237{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4}-.0323685{col 67}{space 3} .3307098
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 8.640825{col 26}{space 2} 6.305225{col 37}{space 1}    1.37{col 46}{space 3}0.171{col 54}{space 4}-3.717188{col 67}{space 3} 20.99884
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit un_rate Tradelib Private unemployment Education Female Income employed Age EqualityP, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-302.97645}  
Iteration 1:{space 3}log pseudolikelihood = {res:-287.88868}  
Iteration 2:{space 3}log pseudolikelihood = {res:-286.65669}  
Iteration 3:{space 3}log pseudolikelihood = {res:-286.65431}  
Iteration 4:{space 3}log pseudolikelihood = {res:-286.65431}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,181}
{txt}{col 57}{lalign 13:Wald chi2({res:9})}{col 70} = {res}{ralign 6:31.01}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0003}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-286.65431}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0539}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     un_rate{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Tradelib {c |}{col 14}{res}{space 2}-.0112554{col 26}{space 2}  .006843{col 37}{space 1}   -1.64{col 46}{space 3}0.100{col 54}{space 4}-.0246675{col 67}{space 3} .0021567
{txt}{space 5}Private {c |}{col 14}{res}{space 2}-.2507084{col 26}{space 2} .2961627{col 37}{space 1}   -0.85{col 46}{space 3}0.397{col 54}{space 4}-.8311766{col 67}{space 3} .3297597
{txt}unemployment {c |}{col 14}{res}{space 2}-1.050336{col 26}{space 2} .4178102{col 37}{space 1}   -2.51{col 46}{space 3}0.012{col 54}{space 4}-1.869229{col 67}{space 3} -.231443
{txt}{space 3}Education {c |}{col 14}{res}{space 2} .0603095{col 26}{space 2}  .068666{col 37}{space 1}    0.88{col 46}{space 3}0.380{col 54}{space 4}-.0742734{col 67}{space 3} .1948924
{txt}{space 6}Female {c |}{col 14}{res}{space 2}-.0473329{col 26}{space 2} .2293474{col 37}{space 1}   -0.21{col 46}{space 3}0.836{col 54}{space 4}-.4968455{col 67}{space 3} .4021797
{txt}{space 6}Income {c |}{col 14}{res}{space 2}  .236966{col 26}{space 2} .0747199{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} .0905176{col 67}{space 3} .3834143
{txt}{space 4}employed {c |}{col 14}{res}{space 2}-.1639089{col 26}{space 2} .2607872{col 37}{space 1}   -0.63{col 46}{space 3}0.530{col 54}{space 4}-.6750425{col 67}{space 3} .3472248
{txt}{space 9}Age {c |}{col 14}{res}{space 2}-.0141137{col 26}{space 2} .0107463{col 37}{space 1}   -1.31{col 46}{space 3}0.189{col 54}{space 4}-.0351759{col 67}{space 3} .0069486
{txt}{space 3}EqualityP {c |}{col 14}{res}{space 2}-.0150016{col 26}{space 2} .0407959{col 37}{space 1}   -0.37{col 46}{space 3}0.713{col 54}{space 4}  -.09496{col 67}{space 3} .0649569
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  6.25564{col 26}{space 2} 1.993795{col 37}{space 1}    3.14{col 46}{space 3}0.002{col 54}{space 4} 2.347874{col 67}{space 3} 10.16341
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. logit gov_rate Tradelib Private unemployment Education Female Income employed Age EqualityP, robust or 

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-32.312795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-30.599808}  
Iteration 2:{space 3}log pseudolikelihood = {res:-29.375303}  
Iteration 3:{space 3}log pseudolikelihood = {res:-29.373125}  
Iteration 4:{space 3}log pseudolikelihood = {res:-29.373124}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,181}
{txt}{col 57}{lalign 13:Wald chi2({res:9})}{col 70} = {res}{ralign 6:144.78}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-29.373124}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0910}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    gov_rate{col 14}{c |} Odds ratio{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Tradelib {c |}{col 14}{res}{space 2} .9867941{col 26}{space 2} .0102205{col 37}{space 1}   -1.28{col 46}{space 3}0.199{col 54}{space 4} .9669642{col 67}{space 3} 1.007031
{txt}{space 5}Private {c |}{col 14}{res}{space 2} 1.313659{col 26}{space 2}  1.23528{col 37}{space 1}    0.29{col 46}{space 3}0.772{col 54}{space 4}  .208002{col 67}{space 3} 8.296552
{txt}unemployment {c |}{col 14}{res}{space 2} .2988542{col 26}{space 2} .3463719{col 37}{space 1}   -1.04{col 46}{space 3}0.297{col 54}{space 4}  .030826{col 67}{space 3} 2.897355
{txt}{space 3}Education {c |}{col 14}{res}{space 2} 1.001034{col 26}{space 2} .0879211{col 37}{space 1}    0.01{col 46}{space 3}0.991{col 54}{space 4} .8427282{col 67}{space 3} 1.189077
{txt}{space 6}Female {c |}{col 14}{res}{space 2} .6671434{col 26}{space 2} .5402667{col 37}{space 1}   -0.50{col 46}{space 3}0.617{col 54}{space 4} .1364263{col 67}{space 3} 3.262422
{txt}{space 6}Income {c |}{col 14}{res}{space 2} 1.030735{col 26}{space 2} .2071009{col 37}{space 1}    0.15{col 46}{space 3}0.880{col 54}{space 4} .6952148{col 67}{space 3} 1.528182
{txt}{space 4}employed {c |}{col 14}{res}{space 2} 5.667264{col 26}{space 2} 6.435943{col 37}{space 1}    1.53{col 46}{space 3}0.127{col 54}{space 4} .6119546{col 67}{space 3} 52.48409
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .9964021{col 26}{space 2} .0244283{col 37}{space 1}   -0.15{col 46}{space 3}0.883{col 54}{space 4} .9496556{col 67}{space 3}  1.04545
{txt}{space 3}EqualityP {c |}{col 14}{res}{space 2} 1.160871{col 26}{space 2} .1075242{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4} .9681498{col 67}{space 3} 1.391956
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5657.998{col 26}{space 2} 35674.95{col 37}{space 1}    1.37{col 46}{space 3}0.171{col 54}{space 4} .0243022{col 67}{space 3} 1.32e+09
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds{txt}.{p_end}

{com}. logit un_rate Tradelib Private unemployment Education Female Income employed Age EqualityP, robust or

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-302.97645}  
Iteration 1:{space 3}log pseudolikelihood = {res:-287.88868}  
Iteration 2:{space 3}log pseudolikelihood = {res:-286.65669}  
Iteration 3:{space 3}log pseudolikelihood = {res:-286.65431}  
Iteration 4:{space 3}log pseudolikelihood = {res:-286.65431}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,181}
{txt}{col 57}{lalign 13:Wald chi2({res:9})}{col 70} = {res}{ralign 6:31.01}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0003}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-286.65431}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0539}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     un_rate{col 14}{c |} Odds ratio{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}Tradelib {c |}{col 14}{res}{space 2} .9888077{col 26}{space 2} .0067664{col 37}{space 1}   -1.64{col 46}{space 3}0.100{col 54}{space 4} .9756343{col 67}{space 3} 1.002159
{txt}{space 5}Private {c |}{col 14}{res}{space 2} .7782493{col 26}{space 2} .2304884{col 37}{space 1}   -0.85{col 46}{space 3}0.397{col 54}{space 4} .4355366{col 67}{space 3} 1.390634
{txt}unemployment {c |}{col 14}{res}{space 2} .3498202{col 26}{space 2} .1461585{col 37}{space 1}   -2.51{col 46}{space 3}0.012{col 54}{space 4} .1542425{col 67}{space 3} .7933879
{txt}{space 3}Education {c |}{col 14}{res}{space 2} 1.062165{col 26}{space 2} .0729346{col 37}{space 1}    0.88{col 46}{space 3}0.380{col 54}{space 4} .9284178{col 67}{space 3}  1.21518
{txt}{space 6}Female {c |}{col 14}{res}{space 2} .9537698{col 26}{space 2} .2187446{col 37}{space 1}   -0.21{col 46}{space 3}0.836{col 54}{space 4}  .608447{col 67}{space 3}  1.49508
{txt}{space 6}Income {c |}{col 14}{res}{space 2} 1.267398{col 26}{space 2} .0946999{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} 1.094741{col 67}{space 3} 1.467286
{txt}{space 4}employed {c |}{col 14}{res}{space 2} .8488194{col 26}{space 2} .2213613{col 37}{space 1}   -0.63{col 46}{space 3}0.530{col 54}{space 4} .5091348{col 67}{space 3} 1.415135
{txt}{space 9}Age {c |}{col 14}{res}{space 2} .9859855{col 26}{space 2} .0105956{col 37}{space 1}   -1.31{col 46}{space 3}0.189{col 54}{space 4} .9654355{col 67}{space 3} 1.006973
{txt}{space 3}EqualityP {c |}{col 14}{res}{space 2} .9851104{col 26}{space 2} .0401884{col 37}{space 1}   -0.37{col 46}{space 3}0.713{col 54}{space 4} .9094093{col 67}{space 3} 1.067113
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 520.9426{col 26}{space 2} 1038.653{col 37}{space 1}    3.14{col 46}{space 3}0.002{col 54}{space 4}  10.4633{col 67}{space 3} 25936.48
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. **Ologit with cluster standard error, Table A7, A8
. 
. ologit Govresp Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2534.9073}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2355.0284}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2348.5807}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2348.5611}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2348.5611}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,144}
{txt}{col 57}{lalign 13:Wald chi2({res:12})}{col 70} = {res}{ralign 6:859.52}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2348.5611}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0735}

{txt}{ralign 82:(Std. err. adjusted for {res:24} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0188832{col 30}{space 2} .0048932{col 41}{space 1}    3.86{col 50}{space 3}0.000{col 58}{space 4} .0092927{col 71}{space 3} .0284737
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3170404{col 30}{space 2}  .144894{col 41}{space 1}   -2.19{col 50}{space 3}0.029{col 58}{space 4}-.6010274{col 71}{space 3}-.0330533
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5049408{col 30}{space 2} .2223714{col 41}{space 1}    2.27{col 50}{space 3}0.023{col 58}{space 4} .0691009{col 71}{space 3} .9407807
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0003012{col 30}{space 2} .0027134{col 41}{space 1}   -0.11{col 50}{space 3}0.912{col 58}{space 4}-.0056194{col 71}{space 3}  .005017
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0550923{col 30}{space 2} .0147615{col 41}{space 1}   -3.73{col 50}{space 3}0.000{col 58}{space 4}-.0840243{col 71}{space 3}-.0261603
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0461089{col 30}{space 2} .0347821{col 41}{space 1}   -1.33{col 50}{space 3}0.185{col 58}{space 4}-.1142806{col 71}{space 3} .0220628
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0593303{col 30}{space 2} .1247816{col 41}{space 1}   -0.48{col 50}{space 3}0.634{col 58}{space 4}-.3038977{col 71}{space 3} .1852371
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1078782{col 30}{space 2} .0891208{col 41}{space 1}   -1.21{col 50}{space 3}0.226{col 58}{space 4}-.2825517{col 71}{space 3} .0667953
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0023196{col 30}{space 2} .0423965{col 41}{space 1}    0.05{col 50}{space 3}0.956{col 58}{space 4} -.080776{col 71}{space 3} .0854153
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0346469{col 30}{space 2} .0251407{col 41}{space 1}   -1.38{col 50}{space 3}0.168{col 58}{space 4}-.0839218{col 71}{space 3}  .014628
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0018269{col 30}{space 2} .0046531{col 41}{space 1}    0.39{col 50}{space 3}0.695{col 58}{space 4}-.0072929{col 71}{space 3} .0109467
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3970744{col 30}{space 2} .0291651{col 41}{space 1}   13.61{col 50}{space 3}0.000{col 58}{space 4} .3399118{col 71}{space 3} .4542369
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-1.859009{col 30}{space 2} 1.161037{col 58}{space 4}  -4.1346{col 71}{space 3} .4165815
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-.7545387{col 30}{space 2} 1.243092{col 58}{space 4}-3.190955{col 71}{space 3} 1.681877
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2} .1770154{col 30}{space 2} 1.217331{col 58}{space 4}-2.208909{col 71}{space 3} 2.562939
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2} .8514179{col 30}{space 2}  1.18087{col 58}{space 4}-1.463045{col 71}{space 3} 3.165881
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2} 1.391777{col 30}{space 2} 1.154068{col 58}{space 4} -.870155{col 71}{space 3} 3.653708
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2} 1.888685{col 30}{space 2} 1.170778{col 58}{space 4}-.4059982{col 71}{space 3} 4.183368
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} 2.518703{col 30}{space 2} 1.160138{col 58}{space 4} .2448742{col 71}{space 3} 4.792533
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 3.631616{col 30}{space 2} 1.103284{col 58}{space 4} 1.469218{col 71}{space 3} 5.794013
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 4.290073{col 30}{space 2} 1.076369{col 58}{space 4} 2.180428{col 71}{space 3} 6.399717
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit Unemploy Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1882.9868}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1857.2264}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1857.1524}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1857.1524}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,071}
{txt}{col 57}{lalign 13:Wald chi2({res:12})}{col 70} = {res}{ralign 6:47.03}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1857.1524}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0137}

{txt}{ralign 82:(Std. err. adjusted for {res:23} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0240275{col 30}{space 2} .0093856{col 41}{space 1}    2.56{col 50}{space 3}0.010{col 58}{space 4} .0056321{col 71}{space 3}  .042423
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.1133413{col 30}{space 2}   .12936{col 41}{space 1}   -0.88{col 50}{space 3}0.381{col 58}{space 4}-.3668823{col 71}{space 3} .1401997
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2}  .798527{col 30}{space 2} .5000913{col 41}{space 1}    1.60{col 50}{space 3}0.110{col 58}{space 4} -.181634{col 71}{space 3} 1.778688
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0159689{col 30}{space 2} .0121012{col 41}{space 1}   -1.32{col 50}{space 3}0.187{col 58}{space 4}-.0396869{col 71}{space 3} .0077492
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0184712{col 30}{space 2}  .015037{col 41}{space 1}   -1.23{col 50}{space 3}0.219{col 58}{space 4}-.0479433{col 71}{space 3} .0110009
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.1001879{col 30}{space 2} .0578114{col 41}{space 1}   -1.73{col 50}{space 3}0.083{col 58}{space 4}-.2134962{col 71}{space 3} .0131203
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0615394{col 30}{space 2} .1287622{col 41}{space 1}   -0.48{col 50}{space 3}0.633{col 58}{space 4}-.3139087{col 71}{space 3} .1908298
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.2112003{col 30}{space 2} .1272692{col 41}{space 1}   -1.66{col 50}{space 3}0.097{col 58}{space 4}-.4606434{col 71}{space 3} .0382428
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0313226{col 30}{space 2} .0418004{col 41}{space 1}   -0.75{col 50}{space 3}0.454{col 58}{space 4}-.1132499{col 71}{space 3} .0506047
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0252742{col 30}{space 2} .0266639{col 41}{space 1}    0.95{col 50}{space 3}0.343{col 58}{space 4} -.026986{col 71}{space 3} .0775345
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0036402{col 30}{space 2} .0053745{col 41}{space 1}   -0.68{col 50}{space 3}0.498{col 58}{space 4}-.0141741{col 71}{space 3} .0068937
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0760227{col 30}{space 2} .0559344{col 41}{space 1}    1.36{col 50}{space 3}0.174{col 58}{space 4}-.0336067{col 71}{space 3} .1856521
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-2.085487{col 30}{space 2} 2.237768{col 58}{space 4}-6.471433{col 71}{space 3} 2.300458
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-1.474369{col 30}{space 2} 2.306334{col 58}{space 4}  -5.9947{col 71}{space 3} 3.045963
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}-.8748711{col 30}{space 2} 2.317675{col 58}{space 4}-5.417431{col 71}{space 3} 3.667689
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2}-.6118154{col 30}{space 2} 2.291754{col 58}{space 4} -5.10357{col 71}{space 3}  3.87994
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2}-.3067677{col 30}{space 2} 2.269289{col 58}{space 4}-4.754492{col 71}{space 3} 4.140956
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2} .0674892{col 30}{space 2}  2.24001{col 58}{space 4} -4.32285{col 71}{space 3} 4.457828
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} .6587317{col 30}{space 2} 2.219278{col 58}{space 4}-3.690973{col 71}{space 3} 5.008436
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 1.789976{col 30}{space 2} 2.201437{col 58}{space 4} -2.52476{col 71}{space 3} 6.104713
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 2.837794{col 30}{space 2} 2.206824{col 58}{space 4}-1.487503{col 71}{space 3}  7.16309
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit Govresp Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2534.9073}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2354.9951}  
Iteration 2:{space 3}log pseudolikelihood = {res: -2348.523}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2348.5033}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2348.5033}  
{res}
{txt}{col 1}Ordered logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:1,144}
{txt}{col 56}{lalign 13:Wald chi2({res:13})}{col 69} = {res}{ralign 7:1088.40}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2348.5033}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0735}

{txt}{ralign 82:(Std. err. adjusted for {res:24} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}Exportlib {c |}{col 18}{res}{space 2} .0199794{col 30}{space 2} .0037898{col 41}{space 1}    5.27{col 50}{space 3}0.000{col 58}{space 4} .0125514{col 71}{space 3} .0274073
{txt}{space 7}Importlib {c |}{col 18}{res}{space 2} .0173823{col 30}{space 2}  .009189{col 41}{space 1}    1.89{col 50}{space 3}0.059{col 58}{space 4}-.0006279{col 71}{space 3} .0353925
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3147978{col 30}{space 2} .1452984{col 41}{space 1}   -2.17{col 50}{space 3}0.030{col 58}{space 4}-.5995775{col 71}{space 3}-.0300182
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .4948016{col 30}{space 2} .2359322{col 41}{space 1}    2.10{col 50}{space 3}0.036{col 58}{space 4}  .032383{col 71}{space 3} .9572202
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} -.000792{col 30}{space 2} .0027165{col 41}{space 1}   -0.29{col 50}{space 3}0.771{col 58}{space 4}-.0061163{col 71}{space 3} .0045323
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0520786{col 30}{space 2} .0233964{col 41}{space 1}   -2.23{col 50}{space 3}0.026{col 58}{space 4}-.0979347{col 71}{space 3}-.0062225
{txt}{space 10}Income {c |}{col 18}{res}{space 2} -.045979{col 30}{space 2} .0348466{col 41}{space 1}   -1.32{col 50}{space 3}0.187{col 58}{space 4}-.1142772{col 71}{space 3} .0223192
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0612452{col 30}{space 2} .1264475{col 41}{space 1}   -0.48{col 50}{space 3}0.628{col 58}{space 4}-.3090777{col 71}{space 3} .1865873
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1080918{col 30}{space 2} .0894768{col 41}{space 1}   -1.21{col 50}{space 3}0.227{col 58}{space 4}-.2834631{col 71}{space 3} .0672794
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0015585{col 30}{space 2} .0417105{col 41}{space 1}    0.04{col 50}{space 3}0.970{col 58}{space 4}-.0801927{col 71}{space 3} .0833097
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0350153{col 30}{space 2} .0248526{col 41}{space 1}   -1.41{col 50}{space 3}0.159{col 58}{space 4}-.0837256{col 71}{space 3}  .013695
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0018636{col 30}{space 2} .0047031{col 41}{space 1}    0.40{col 50}{space 3}0.692{col 58}{space 4}-.0073543{col 71}{space 3} .0110815
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3974868{col 30}{space 2} .0285927{col 41}{space 1}   13.90{col 50}{space 3}0.000{col 58}{space 4} .3414461{col 71}{space 3} .4535274
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-1.791364{col 30}{space 2} 1.260334{col 58}{space 4}-4.261573{col 71}{space 3} .6788457
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-.6868147{col 30}{space 2} 1.330669{col 58}{space 4}-3.294877{col 71}{space 3} 1.921248
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2} .2455745{col 30}{space 2} 1.304369{col 58}{space 4}-2.310941{col 71}{space 3}  2.80209
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2} .9206665{col 30}{space 2} 1.260575{col 58}{space 4}-1.550015{col 71}{space 3} 3.391348
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2} 1.461226{col 30}{space 2} 1.228505{col 58}{space 4}   -.9466{col 71}{space 3} 3.869052
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2} 1.958077{col 30}{space 2} 1.246697{col 58}{space 4} -.485405{col 71}{space 3} 4.401559
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} 2.587671{col 30}{space 2} 1.228477{col 58}{space 4} .1799001{col 71}{space 3} 4.995442
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 3.699996{col 30}{space 2} 1.158475{col 58}{space 4} 1.429425{col 71}{space 3} 5.970566
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 4.358426{col 30}{space 2} 1.128648{col 58}{space 4} 2.146317{col 71}{space 3} 6.570536
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit Unemploy Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1882.9868}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1850.8677}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1850.7432}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1850.7432}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,071}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:22.60}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0468}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1850.7432}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0171}

{txt}{ralign 82:(Std. err. adjusted for {res:23} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}Exportlib {c |}{col 18}{res}{space 2} .0116912{col 30}{space 2} .0119318{col 41}{space 1}    0.98{col 50}{space 3}0.327{col 58}{space 4}-.0116947{col 71}{space 3} .0350771
{txt}{space 7}Importlib {c |}{col 18}{res}{space 2} .0404466{col 30}{space 2} .0160904{col 41}{space 1}    2.51{col 50}{space 3}0.012{col 58}{space 4} .0089101{col 71}{space 3} .0719831
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.1541818{col 30}{space 2} .1368241{col 41}{space 1}   -1.13{col 50}{space 3}0.260{col 58}{space 4}-.4223522{col 71}{space 3} .1139885
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .9156843{col 30}{space 2}  .487599{col 41}{space 1}    1.88{col 50}{space 3}0.060{col 58}{space 4}-.0399922{col 71}{space 3} 1.871361
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0100018{col 30}{space 2} .0129165{col 41}{space 1}   -0.77{col 50}{space 3}0.439{col 58}{space 4}-.0353176{col 71}{space 3}  .015314
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0523091{col 30}{space 2} .0316105{col 41}{space 1}   -1.65{col 50}{space 3}0.098{col 58}{space 4}-.1142644{col 71}{space 3} .0096463
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.1067324{col 30}{space 2} .0540736{col 41}{space 1}   -1.97{col 50}{space 3}0.048{col 58}{space 4}-.2127146{col 71}{space 3}-.0007502
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0413905{col 30}{space 2} .1248271{col 41}{space 1}   -0.33{col 50}{space 3}0.740{col 58}{space 4} -.286047{col 71}{space 3} .2032661
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.2113907{col 30}{space 2} .1267001{col 41}{space 1}   -1.67{col 50}{space 3}0.095{col 58}{space 4}-.4597184{col 71}{space 3}  .036937
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0273161{col 30}{space 2} .0416896{col 41}{space 1}   -0.66{col 50}{space 3}0.512{col 58}{space 4}-.1090262{col 71}{space 3} .0543939
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}  .029061{col 30}{space 2} .0266874{col 41}{space 1}    1.09{col 50}{space 3}0.276{col 58}{space 4}-.0232454{col 71}{space 3} .0813674
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0045862{col 30}{space 2} .0056756{col 41}{space 1}   -0.81{col 50}{space 3}0.419{col 58}{space 4}-.0157102{col 71}{space 3} .0065377
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0733155{col 30}{space 2} .0539674{col 41}{space 1}    1.36{col 50}{space 3}0.174{col 58}{space 4}-.0324587{col 71}{space 3} .1790898
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-2.942056{col 30}{space 2} 2.117446{col 58}{space 4}-7.092173{col 71}{space 3} 1.208062
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2} -2.32769{col 30}{space 2}  2.15528{col 58}{space 4}-6.551961{col 71}{space 3} 1.896581
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}-1.721859{col 30}{space 2} 2.167383{col 58}{space 4}-5.969852{col 71}{space 3} 2.526134
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2}-1.455992{col 30}{space 2} 2.151415{col 58}{space 4}-5.672688{col 71}{space 3} 2.760704
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2}-1.148232{col 30}{space 2} 2.127044{col 58}{space 4}-5.317161{col 71}{space 3} 3.020696
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2}-.7697156{col 30}{space 2} 2.087544{col 58}{space 4}-4.861227{col 71}{space 3} 3.321796
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2}-.1718533{col 30}{space 2} 2.059806{col 58}{space 4}   -4.209{col 71}{space 3} 3.865293
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2}  .970984{col 30}{space 2} 2.035982{col 58}{space 4}-3.019468{col 71}{space 3} 4.961436
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2}  2.02346{col 30}{space 2} 2.029919{col 58}{space 4}-1.955107{col 71}{space 3} 6.002028
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. **Ologit with province-level fixed effect, Table A9
. 
. xi: ologit Govresp Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP i.V256, cluster(V256)
{txt}i.V256{col 19}_IV256_156001-156034{col 39}(naturally coded; _IV256_156001 omitted)

note: {bf:_IV256_156024} omitted because of collinearity.
note: {bf:_IV256_156032} omitted because of collinearity.
note: {bf:_IV256_156033} omitted because of collinearity.
note: {bf:_IV256_156034} omitted because of collinearity.
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2534.9073}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2335.4482}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2325.1513}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2325.0969}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2325.0969}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,144}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(9)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2325.0969}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0828}

{txt}{ralign 82:(Std. err. adjusted for {res:24} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .1028485{col 30}{space 2} .0566626{col 41}{space 1}    1.82{col 50}{space 3}0.070{col 58}{space 4}-.0082082{col 71}{space 3} .2139052
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3772477{col 30}{space 2}  .157786{col 41}{space 1}   -2.39{col 50}{space 3}0.017{col 58}{space 4}-.6865025{col 71}{space 3}-.0679929
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2}-4.430413{col 30}{space 2} 3.028958{col 41}{space 1}   -1.46{col 50}{space 3}0.144{col 58}{space 4}-10.36706{col 71}{space 3} 1.506235
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} .0301686{col 30}{space 2} .0086696{col 41}{space 1}    3.48{col 50}{space 3}0.001{col 58}{space 4} .0131764{col 71}{space 3} .0471608
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.6209443{col 30}{space 2} .3438366{col 41}{space 1}   -1.81{col 50}{space 3}0.071{col 58}{space 4}-1.294852{col 71}{space 3} .0529631
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0274706{col 30}{space 2} .0383768{col 41}{space 1}   -0.72{col 50}{space 3}0.474{col 58}{space 4}-.1026878{col 71}{space 3} .0477466
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.1038131{col 30}{space 2} .1291874{col 41}{space 1}   -0.80{col 50}{space 3}0.422{col 58}{space 4}-.3570156{col 71}{space 3} .1493895
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1118323{col 30}{space 2}  .091265{col 41}{space 1}   -1.23{col 50}{space 3}0.220{col 58}{space 4}-.2907084{col 71}{space 3} .0670439
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0017829{col 30}{space 2} .0433382{col 41}{space 1}   -0.04{col 50}{space 3}0.967{col 58}{space 4}-.0867243{col 71}{space 3} .0831585
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0533062{col 30}{space 2}   .02629{col 41}{space 1}   -2.03{col 50}{space 3}0.043{col 58}{space 4}-.1048337{col 71}{space 3}-.0017788
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0020946{col 30}{space 2}   .00478{col 41}{space 1}    0.44{col 50}{space 3}0.661{col 58}{space 4}-.0072741{col 71}{space 3} .0114633
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3852318{col 30}{space 2}  .029011{col 41}{space 1}   13.28{col 50}{space 3}0.000{col 58}{space 4} .3283712{col 71}{space 3} .4420924
{txt}{space 3}_IV256_156002 {c |}{col 18}{res}{space 2} 1.305336{col 30}{space 2} .4529133{col 41}{space 1}    2.88{col 50}{space 3}0.004{col 58}{space 4} .4176418{col 71}{space 3} 2.193029
{txt}{space 3}_IV256_156003 {c |}{col 18}{res}{space 2}  1.69937{col 30}{space 2} .9232073{col 41}{space 1}    1.84{col 50}{space 3}0.066{col 58}{space 4} -.110083{col 71}{space 3} 3.508823
{txt}{space 3}_IV256_156005 {c |}{col 18}{res}{space 2}-.2960067{col 30}{space 2}  .477508{col 41}{space 1}   -0.62{col 50}{space 3}0.535{col 58}{space 4}-1.231905{col 71}{space 3} .6398918
{txt}{space 3}_IV256_156006 {c |}{col 18}{res}{space 2} .1472885{col 30}{space 2}  .101103{col 41}{space 1}    1.46{col 50}{space 3}0.145{col 58}{space 4}-.0508696{col 71}{space 3} .3454467
{txt}{space 3}_IV256_156007 {c |}{col 18}{res}{space 2} 4.448952{col 30}{space 2} 2.789306{col 41}{space 1}    1.60{col 50}{space 3}0.111{col 58}{space 4}-1.017988{col 71}{space 3} 9.915892
{txt}{space 3}_IV256_156008 {c |}{col 18}{res}{space 2} -.536884{col 30}{space 2} 1.816507{col 41}{space 1}   -0.30{col 50}{space 3}0.768{col 58}{space 4}-4.097172{col 71}{space 3} 3.023404
{txt}{space 3}_IV256_156009 {c |}{col 18}{res}{space 2}-3.510739{col 30}{space 2} 1.234698{col 41}{space 1}   -2.84{col 50}{space 3}0.004{col 58}{space 4}-5.930703{col 71}{space 3}-1.090776
{txt}{space 3}_IV256_156010 {c |}{col 18}{res}{space 2} -1.73577{col 30}{space 2} .7420553{col 41}{space 1}   -2.34{col 50}{space 3}0.019{col 58}{space 4}-3.190172{col 71}{space 3}-.2813684
{txt}{space 3}_IV256_156011 {c |}{col 18}{res}{space 2}-4.311124{col 30}{space 2} 1.007797{col 41}{space 1}   -4.28{col 50}{space 3}0.000{col 58}{space 4} -6.28637{col 71}{space 3}-2.335878
{txt}{space 3}_IV256_156012 {c |}{col 18}{res}{space 2}-1.263177{col 30}{space 2} .4602078{col 41}{space 1}   -2.74{col 50}{space 3}0.006{col 58}{space 4}-2.165168{col 71}{space 3}-.3611867
{txt}{space 3}_IV256_156013 {c |}{col 18}{res}{space 2}-4.316052{col 30}{space 2} 2.466558{col 41}{space 1}   -1.75{col 50}{space 3}0.080{col 58}{space 4}-9.150418{col 71}{space 3}  .518313
{txt}{space 3}_IV256_156014 {c |}{col 18}{res}{space 2}-.0710926{col 30}{space 2} .1971085{col 41}{space 1}   -0.36{col 50}{space 3}0.718{col 58}{space 4}-.4574181{col 71}{space 3} .3152329
{txt}{space 3}_IV256_156015 {c |}{col 18}{res}{space 2}-3.270453{col 30}{space 2}  2.22575{col 41}{space 1}   -1.47{col 50}{space 3}0.142{col 58}{space 4}-7.632842{col 71}{space 3} 1.091936
{txt}{space 3}_IV256_156016 {c |}{col 18}{res}{space 2} 3.487892{col 30}{space 2} 2.069703{col 41}{space 1}    1.69{col 50}{space 3}0.092{col 58}{space 4}-.5686518{col 71}{space 3} 7.544435
{txt}{space 3}_IV256_156017 {c |}{col 18}{res}{space 2} 5.378191{col 30}{space 2} 3.216141{col 41}{space 1}    1.67{col 50}{space 3}0.094{col 58}{space 4}-.9253302{col 71}{space 3} 11.68171
{txt}{space 3}_IV256_156018 {c |}{col 18}{res}{space 2}-8.292597{col 30}{space 2} 4.891312{col 41}{space 1}   -1.70{col 50}{space 3}0.090{col 58}{space 4}-17.87939{col 71}{space 3} 1.294197
{txt}{space 3}_IV256_156019 {c |}{col 18}{res}{space 2}-.3597763{col 30}{space 2} .5339106{col 41}{space 1}   -0.67{col 50}{space 3}0.500{col 58}{space 4}-1.406222{col 71}{space 3} .6866693
{txt}{space 3}_IV256_156020 {c |}{col 18}{res}{space 2} 2.110054{col 30}{space 2} 1.173985{col 41}{space 1}    1.80{col 50}{space 3}0.072{col 58}{space 4}-.1909144{col 71}{space 3} 4.411022
{txt}{space 3}_IV256_156021 {c |}{col 18}{res}{space 2} 8.246682{col 30}{space 2}  4.47171{col 41}{space 1}    1.84{col 50}{space 3}0.065{col 58}{space 4}-.5177082{col 71}{space 3} 17.01107
{txt}{space 3}_IV256_156024 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 3}_IV256_156032 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 3}_IV256_156033 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 3}_IV256_156034 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-38.50802{col 30}{space 2} 22.92245{col 58}{space 4} -83.4352{col 71}{space 3} 6.419159
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-37.35916{col 30}{space 2} 22.85425{col 58}{space 4}-82.15266{col 71}{space 3} 7.434351
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}-36.39729{col 30}{space 2} 22.86936{col 58}{space 4}-81.22041{col 71}{space 3} 8.425826
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2}-35.69935{col 30}{space 2} 22.82868{col 58}{space 4}-80.44273{col 71}{space 3} 9.044032
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2}-35.14111{col 30}{space 2} 22.81263{col 58}{space 4}-79.85305{col 71}{space 3}  9.57082
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2}-34.63204{col 30}{space 2} 22.82147{col 58}{space 4} -79.3613{col 71}{space 3} 10.09721
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2}-33.98832{col 30}{space 2} 22.77845{col 58}{space 4}-78.63326{col 71}{space 3} 10.65661
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} -32.8575{col 30}{space 2}  22.6911{col 58}{space 4}-77.33123{col 71}{space 3} 11.61623
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2}-32.19768{col 30}{space 2} 22.63928{col 58}{space 4}-76.56986{col 71}{space 3}  12.1745
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. xi: ologit Unemploy Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP i.V256, cluster(V256)
{txt}i.V256{col 19}_IV256_156001-156034{col 39}(naturally coded; _IV256_156001 omitted)

note: {bf:_IV256_156008} omitted because of collinearity.
note: {bf:_IV256_156024} omitted because of collinearity.
note: {bf:_IV256_156032} omitted because of collinearity.
note: {bf:_IV256_156033} omitted because of collinearity.
note: {bf:_IV256_156034} omitted because of collinearity.
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1882.9868}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1812.8126}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1811.5302}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1811.5285}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1811.5285}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,071}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(10)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1811.5285}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0379}

{txt}{ralign 82:(Std. err. adjusted for {res:23} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .3074493{col 30}{space 2} .0788019{col 41}{space 1}    3.90{col 50}{space 3}0.000{col 58}{space 4} .1530004{col 71}{space 3} .4618983
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.2243605{col 30}{space 2}  .148118{col 41}{space 1}   -1.51{col 50}{space 3}0.130{col 58}{space 4}-.5146664{col 71}{space 3} .0659455
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2}-8.235392{col 30}{space 2} 4.255232{col 41}{space 1}   -1.94{col 50}{space 3}0.053{col 58}{space 4}-16.57549{col 71}{space 3} .1047099
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.1224944{col 30}{space 2}  .014785{col 41}{space 1}   -8.29{col 50}{space 3}0.000{col 58}{space 4}-.1514726{col 71}{space 3}-.0935162
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-1.318543{col 30}{space 2} .4758243{col 41}{space 1}   -2.77{col 50}{space 3}0.006{col 58}{space 4}-2.251141{col 71}{space 3}-.3859443
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0703049{col 30}{space 2} .0471093{col 41}{space 1}   -1.49{col 50}{space 3}0.136{col 58}{space 4}-.1626374{col 71}{space 3} .0220276
{txt}{space 8}employed {c |}{col 18}{res}{space 2} -.058385{col 30}{space 2} .1299079{col 41}{space 1}   -0.45{col 50}{space 3}0.653{col 58}{space 4}-.3129998{col 71}{space 3} .1962298
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1940067{col 30}{space 2} .1277106{col 41}{space 1}   -1.52{col 50}{space 3}0.129{col 58}{space 4}-.4443148{col 71}{space 3} .0563014
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0473226{col 30}{space 2} .0418378{col 41}{space 1}   -1.13{col 50}{space 3}0.258{col 58}{space 4}-.1293232{col 71}{space 3}  .034678
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0339079{col 30}{space 2} .0289449{col 41}{space 1}    1.17{col 50}{space 3}0.241{col 58}{space 4}-.0228231{col 71}{space 3} .0906389
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0042878{col 30}{space 2} .0050698{col 41}{space 1}   -0.85{col 50}{space 3}0.398{col 58}{space 4}-.0142245{col 71}{space 3} .0056489
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0787654{col 30}{space 2} .0542591{col 41}{space 1}    1.45{col 50}{space 3}0.147{col 58}{space 4}-.0275805{col 71}{space 3} .1851113
{txt}{space 3}_IV256_156002 {c |}{col 18}{res}{space 2} 1.146008{col 30}{space 2} .6013982{col 41}{space 1}    1.91{col 50}{space 3}0.057{col 58}{space 4} -.032711{col 71}{space 3} 2.324726
{txt}{space 3}_IV256_156003 {c |}{col 18}{res}{space 2} 4.389122{col 30}{space 2} 1.265365{col 41}{space 1}    3.47{col 50}{space 3}0.001{col 58}{space 4} 1.909053{col 71}{space 3} 6.869191
{txt}{space 3}_IV256_156005 {c |}{col 18}{res}{space 2}  2.58852{col 30}{space 2} .5845738{col 41}{space 1}    4.43{col 50}{space 3}0.000{col 58}{space 4} 1.442777{col 71}{space 3} 3.734264
{txt}{space 3}_IV256_156006 {c |}{col 18}{res}{space 2}-1.153319{col 30}{space 2} .1831971{col 41}{space 1}   -6.30{col 50}{space 3}0.000{col 58}{space 4}-1.512379{col 71}{space 3}-.7942595
{txt}{space 3}_IV256_156007 {c |}{col 18}{res}{space 2} 8.698085{col 30}{space 2}  3.84606{col 41}{space 1}    2.26{col 50}{space 3}0.024{col 58}{space 4} 1.159945{col 71}{space 3} 16.23622
{txt}{space 3}_IV256_156008 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 3}_IV256_156009 {c |}{col 18}{res}{space 2}-1.585538{col 30}{space 2} 1.748939{col 41}{space 1}   -0.91{col 50}{space 3}0.365{col 58}{space 4}-5.013396{col 71}{space 3} 1.842321
{txt}{space 3}_IV256_156010 {c |}{col 18}{res}{space 2}-3.447591{col 30}{space 2} 1.023458{col 41}{space 1}   -3.37{col 50}{space 3}0.001{col 58}{space 4}-5.453532{col 71}{space 3}-1.441651
{txt}{space 3}_IV256_156011 {c |}{col 18}{res}{space 2}-3.020276{col 30}{space 2} 1.180186{col 41}{space 1}   -2.56{col 50}{space 3}0.010{col 58}{space 4}-5.333399{col 71}{space 3}-.7071529
{txt}{space 3}_IV256_156012 {c |}{col 18}{res}{space 2}-2.602614{col 30}{space 2}   .65458{col 41}{space 1}   -3.98{col 50}{space 3}0.000{col 58}{space 4}-3.885567{col 71}{space 3}-1.319661
{txt}{space 3}_IV256_156013 {c |}{col 18}{res}{space 2} -5.48596{col 30}{space 2} 3.395586{col 41}{space 1}   -1.62{col 50}{space 3}0.106{col 58}{space 4}-12.14119{col 71}{space 3} 1.169267
{txt}{space 3}_IV256_156014 {c |}{col 18}{res}{space 2}-1.563973{col 30}{space 2} .2931408{col 41}{space 1}   -5.34{col 50}{space 3}0.000{col 58}{space 4}-2.138518{col 71}{space 3}-.9894274
{txt}{space 3}_IV256_156015 {c |}{col 18}{res}{space 2}-7.907018{col 30}{space 2}   3.0609{col 41}{space 1}   -2.58{col 50}{space 3}0.010{col 58}{space 4}-13.90627{col 71}{space 3}-1.907765
{txt}{space 3}_IV256_156016 {c |}{col 18}{res}{space 2}   7.6715{col 30}{space 2} 2.851551{col 41}{space 1}    2.69{col 50}{space 3}0.007{col 58}{space 4} 2.082563{col 71}{space 3} 13.26044
{txt}{space 3}_IV256_156017 {c |}{col 18}{res}{space 2} 10.93348{col 30}{space 2} 4.492184{col 41}{space 1}    2.43{col 50}{space 3}0.015{col 58}{space 4} 2.128957{col 71}{space 3}   19.738
{txt}{space 3}_IV256_156018 {c |}{col 18}{res}{space 2}-19.30035{col 30}{space 2} 6.860003{col 41}{space 1}   -2.81{col 50}{space 3}0.005{col 58}{space 4}-32.74571{col 71}{space 3}-5.854987
{txt}{space 3}_IV256_156019 {c |}{col 18}{res}{space 2} -1.98424{col 30}{space 2} .7356517{col 41}{space 1}   -2.70{col 50}{space 3}0.007{col 58}{space 4}-3.426091{col 71}{space 3} -.542389
{txt}{space 3}_IV256_156020 {c |}{col 18}{res}{space 2} 2.408558{col 30}{space 2}  1.64653{col 41}{space 1}    1.46{col 50}{space 3}0.144{col 58}{space 4}-.8185819{col 71}{space 3} 5.635698
{txt}{space 3}_IV256_156021 {c |}{col 18}{res}{space 2} 16.74102{col 30}{space 2} 6.167978{col 41}{space 1}    2.71{col 50}{space 3}0.007{col 58}{space 4} 4.652009{col 71}{space 3} 28.83004
{txt}{space 3}_IV256_156024 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 3}_IV256_156032 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 3}_IV256_156033 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 3}_IV256_156034 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-79.52015{col 30}{space 2} 31.72916{col 58}{space 4}-141.7082{col 71}{space 3}-17.33213
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-78.89393{col 30}{space 2} 31.83239{col 58}{space 4}-141.2843{col 71}{space 3} -16.5036
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}-78.26627{col 30}{space 2} 31.84975{col 58}{space 4}-140.6906{col 71}{space 3}-15.84192
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2}-77.99049{col 30}{space 2} 31.82878{col 58}{space 4}-140.3737{col 71}{space 3}-15.60723
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2}-77.67194{col 30}{space 2} 31.80809{col 58}{space 4}-140.0146{col 71}{space 3}-15.32924
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2}-77.27615{col 30}{space 2} 31.80131{col 58}{space 4}-139.6056{col 71}{space 3}-14.94674
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2}-76.64557{col 30}{space 2} 31.79862{col 58}{space 4}-138.9697{col 71}{space 3}-14.32142
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2}-75.44185{col 30}{space 2} 31.77156{col 58}{space 4} -137.713{col 71}{space 3}-13.17075
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2}-74.33521{col 30}{space 2} 31.74875{col 58}{space 4}-136.5616{col 71}{space 3}-12.10881
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. **Controlling labor union and party membership, Table A10, A11
. 
. ologit Govresp Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP LaborMember PartyMember, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2532.4656}  
Iteration 1:{space 3}log pseudolikelihood = {res: -2352.967}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2346.5136}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2346.4936}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2346.4936}  
{res}
{txt}{col 1}Ordered logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:1,143}
{txt}{col 56}{lalign 13:Wald chi2({res:14})}{col 69} = {res}{ralign 7:1344.32}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2346.4936}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0734}

{txt}{ralign 82:(Std. err. adjusted for {res:24} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0190948{col 30}{space 2} .0050401{col 41}{space 1}    3.79{col 50}{space 3}0.000{col 58}{space 4} .0092164{col 71}{space 3} .0289732
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3216053{col 30}{space 2}  .146323{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4}-.6083931{col 71}{space 3}-.0348175
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5002089{col 30}{space 2} .2267951{col 41}{space 1}    2.21{col 50}{space 3}0.027{col 58}{space 4} .0556985{col 71}{space 3} .9447192
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0006127{col 30}{space 2} .0029243{col 41}{space 1}   -0.21{col 50}{space 3}0.834{col 58}{space 4}-.0063443{col 71}{space 3} .0051189
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0552839{col 30}{space 2} .0146201{col 41}{space 1}   -3.78{col 50}{space 3}0.000{col 58}{space 4}-.0839388{col 71}{space 3}-.0266291
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0456959{col 30}{space 2} .0354685{col 41}{space 1}   -1.29{col 50}{space 3}0.198{col 58}{space 4} -.115213{col 71}{space 3} .0238211
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0586617{col 30}{space 2} .1248099{col 41}{space 1}   -0.47{col 50}{space 3}0.638{col 58}{space 4}-.3032845{col 71}{space 3} .1859612
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1019705{col 30}{space 2} .0874242{col 41}{space 1}   -1.17{col 50}{space 3}0.243{col 58}{space 4}-.2733188{col 71}{space 3} .0693777
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0032828{col 30}{space 2} .0428027{col 41}{space 1}    0.08{col 50}{space 3}0.939{col 58}{space 4} -.080609{col 71}{space 3} .0871745
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0358626{col 30}{space 2} .0257271{col 41}{space 1}   -1.39{col 50}{space 3}0.163{col 58}{space 4}-.0862868{col 71}{space 3} .0145615
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0017873{col 30}{space 2} .0046676{col 41}{space 1}    0.38{col 50}{space 3}0.702{col 58}{space 4} -.007361{col 71}{space 3} .0109356
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3968571{col 30}{space 2} .0293199{col 41}{space 1}   13.54{col 50}{space 3}0.000{col 58}{space 4} .3393911{col 71}{space 3} .4543231
{txt}{space 5}LaborMember {c |}{col 18}{res}{space 2}-.1330984{col 30}{space 2} .1563698{col 41}{space 1}   -0.85{col 50}{space 3}0.395{col 58}{space 4}-.4395777{col 71}{space 3} .1733808
{txt}{space 5}PartyMember {c |}{col 18}{res}{space 2}  .121834{col 30}{space 2} .2443954{col 41}{space 1}    0.50{col 50}{space 3}0.618{col 58}{space 4}-.3571721{col 71}{space 3} .6008402
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-1.885682{col 30}{space 2} 1.194697{col 58}{space 4}-4.227244{col 71}{space 3}  .455881
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-.7823889{col 30}{space 2} 1.280202{col 58}{space 4}-3.291539{col 71}{space 3} 1.726761
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2} .1421188{col 30}{space 2} 1.254933{col 58}{space 4}-2.317505{col 71}{space 3} 2.601742
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2} .8191472{col 30}{space 2} 1.223406{col 58}{space 4}-1.578685{col 71}{space 3} 3.216979
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2} 1.360426{col 30}{space 2} 1.199208{col 58}{space 4}-.9899782{col 71}{space 3} 3.710831
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2} 1.857402{col 30}{space 2} 1.216949{col 58}{space 4}-.5277747{col 71}{space 3} 4.242578
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} 2.487209{col 30}{space 2} 1.207359{col 58}{space 4} .1208294{col 71}{space 3} 4.853589
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 3.599882{col 30}{space 2}  1.14631{col 58}{space 4} 1.353156{col 71}{space 3} 5.846608
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 4.258484{col 30}{space 2} 1.119267{col 58}{space 4}  2.06476{col 71}{space 3} 6.452207
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit Unemploy Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP LaborMember PartyMember, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1879.6871}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1854.3291}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1854.2572}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1854.2572}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,070}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(13)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1854.2572}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0135}

{txt}{ralign 82:(Std. err. adjusted for {res:23} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0238237{col 30}{space 2} .0092942{col 41}{space 1}    2.56{col 50}{space 3}0.010{col 58}{space 4} .0056075{col 71}{space 3}   .04204
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.1270033{col 30}{space 2} .1314221{col 41}{space 1}   -0.97{col 50}{space 3}0.334{col 58}{space 4}-.3845858{col 71}{space 3} .1305792
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .8013843{col 30}{space 2} .5022522{col 41}{space 1}    1.60{col 50}{space 3}0.111{col 58}{space 4}-.1830119{col 71}{space 3} 1.785781
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0155407{col 30}{space 2} .0122202{col 41}{space 1}   -1.27{col 50}{space 3}0.203{col 58}{space 4}-.0394919{col 71}{space 3} .0084104
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0181951{col 30}{space 2} .0153412{col 41}{space 1}   -1.19{col 50}{space 3}0.236{col 58}{space 4}-.0482633{col 71}{space 3} .0118732
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0990659{col 30}{space 2}  .060078{col 41}{space 1}   -1.65{col 50}{space 3}0.099{col 58}{space 4}-.2168167{col 71}{space 3}  .018685
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0673454{col 30}{space 2} .1341494{col 41}{space 1}   -0.50{col 50}{space 3}0.616{col 58}{space 4}-.3302734{col 71}{space 3} .1955827
{txt}{space 10}Female {c |}{col 18}{res}{space 2} -.207605{col 30}{space 2} .1280221{col 41}{space 1}   -1.62{col 50}{space 3}0.105{col 58}{space 4}-.4585237{col 71}{space 3} .0433138
{txt}{space 7}Education {c |}{col 18}{res}{space 2} -.029474{col 30}{space 2} .0427922{col 41}{space 1}   -0.69{col 50}{space 3}0.491{col 58}{space 4}-.1133453{col 71}{space 3} .0543972
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0245292{col 30}{space 2} .0267434{col 41}{space 1}    0.92{col 50}{space 3}0.359{col 58}{space 4}-.0278869{col 71}{space 3} .0769454
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0038313{col 30}{space 2}  .005475{col 41}{space 1}   -0.70{col 50}{space 3}0.484{col 58}{space 4}-.0145621{col 71}{space 3} .0068996
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0748525{col 30}{space 2} .0557354{col 41}{space 1}    1.34{col 50}{space 3}0.179{col 58}{space 4}-.0343868{col 71}{space 3} .1840918
{txt}{space 5}LaborMember {c |}{col 18}{res}{space 2}-.0119226{col 30}{space 2} .1982036{col 41}{space 1}   -0.06{col 50}{space 3}0.952{col 58}{space 4}-.4003946{col 71}{space 3} .3765494
{txt}{space 5}PartyMember {c |}{col 18}{res}{space 2}-.0365649{col 30}{space 2} .2260015{col 41}{space 1}   -0.16{col 50}{space 3}0.871{col 58}{space 4}-.4795196{col 71}{space 3} .4063899
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2} -2.07706{col 30}{space 2} 2.279263{col 58}{space 4}-6.544334{col 71}{space 3} 2.390214
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-1.466048{col 30}{space 2} 2.338265{col 58}{space 4}-6.048962{col 71}{space 3} 3.116867
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}-.8666962{col 30}{space 2} 2.349253{col 58}{space 4}-5.471148{col 71}{space 3} 3.737756
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2} -.603706{col 30}{space 2} 2.323892{col 58}{space 4} -5.15845{col 71}{space 3} 3.951038
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2}-.2987175{col 30}{space 2} 2.301525{col 58}{space 4}-4.809624{col 71}{space 3} 4.212189
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2} .0670612{col 30}{space 2} 2.268678{col 58}{space 4}-4.379466{col 71}{space 3} 4.513589
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} .6602023{col 30}{space 2} 2.249132{col 58}{space 4}-3.748015{col 71}{space 3}  5.06842
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 1.792961{col 30}{space 2} 2.229352{col 58}{space 4}-2.576488{col 71}{space 3}  6.16241
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 2.841352{col 30}{space 2} 2.234493{col 58}{space 4}-1.538174{col 71}{space 3} 7.220877
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit Govresp Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP LaborMember PartyMember, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2532.4656}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2352.9676}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2346.5044}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2346.4844}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2346.4844}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,143}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(14)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2346.4844}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0734}

{txt}{ralign 82:(Std. err. adjusted for {res:24} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}Exportlib {c |}{col 18}{res}{space 2} .0195336{col 30}{space 2} .0037111{col 41}{space 1}    5.26{col 50}{space 3}0.000{col 58}{space 4}   .01226{col 71}{space 3} .0268073
{txt}{space 7}Importlib {c |}{col 18}{res}{space 2} .0184736{col 30}{space 2} .0100523{col 41}{space 1}    1.84{col 50}{space 3}0.066{col 58}{space 4}-.0012284{col 71}{space 3} .0381757
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3205971{col 30}{space 2} .1461894{col 41}{space 1}   -2.19{col 50}{space 3}0.028{col 58}{space 4} -.607123{col 71}{space 3}-.0340711
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2}  .496159{col 30}{space 2} .2415309{col 41}{space 1}    2.05{col 50}{space 3}0.040{col 58}{space 4} .0227671{col 71}{space 3} .9695509
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0008003{col 30}{space 2} .0027595{col 41}{space 1}   -0.29{col 50}{space 3}0.772{col 58}{space 4}-.0062088{col 71}{space 3} .0046082
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0540482{col 30}{space 2} .0245488{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4}-.1021629{col 71}{space 3}-.0059336
{txt}{space 10}Income {c |}{col 18}{res}{space 2} -.045661{col 30}{space 2} .0354737{col 41}{space 1}   -1.29{col 50}{space 3}0.198{col 58}{space 4}-.1151882{col 71}{space 3} .0238662
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0595946{col 30}{space 2} .1269312{col 41}{space 1}   -0.47{col 50}{space 3}0.639{col 58}{space 4}-.3083752{col 71}{space 3}  .189186
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1022067{col 30}{space 2} .0880462{col 41}{space 1}   -1.16{col 50}{space 3}0.246{col 58}{space 4}-.2747741{col 71}{space 3} .0703608
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0029475{col 30}{space 2} .0422281{col 41}{space 1}    0.07{col 50}{space 3}0.944{col 58}{space 4} -.079818{col 71}{space 3}  .085713
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0359884{col 30}{space 2} .0254183{col 41}{space 1}   -1.42{col 50}{space 3}0.157{col 58}{space 4}-.0858073{col 71}{space 3} .0138306
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0017981{col 30}{space 2} .0047044{col 41}{space 1}    0.38{col 50}{space 3}0.702{col 58}{space 4}-.0074224{col 71}{space 3} .0110186
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3970049{col 30}{space 2} .0286584{col 41}{space 1}   13.85{col 50}{space 3}0.000{col 58}{space 4} .3408354{col 71}{space 3} .4531744
{txt}{space 5}LaborMember {c |}{col 18}{res}{space 2}-.1287279{col 30}{space 2} .1693972{col 41}{space 1}   -0.76{col 50}{space 3}0.447{col 58}{space 4}-.4607403{col 71}{space 3} .2032846
{txt}{space 5}PartyMember {c |}{col 18}{res}{space 2}  .118458{col 30}{space 2} .2569044{col 41}{space 1}    0.46{col 50}{space 3}0.645{col 58}{space 4}-.3850654{col 71}{space 3} .6219814
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2} -1.85778{col 30}{space 2} 1.285712{col 58}{space 4}-4.377728{col 71}{space 3} .6621686
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-.7544128{col 30}{space 2} 1.356566{col 58}{space 4}-3.413234{col 71}{space 3} 1.904408
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2} .1704083{col 30}{space 2} 1.328367{col 58}{space 4}-2.433143{col 71}{space 3} 2.773959
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2} .8476682{col 30}{space 2} 1.290336{col 58}{space 4}-1.681344{col 71}{space 3} 3.376681
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2} 1.389011{col 30}{space 2} 1.261241{col 58}{space 4}-1.082977{col 71}{space 3} 3.860999
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2} 1.885963{col 30}{space 2} 1.281273{col 58}{space 4}-.6252849{col 71}{space 3} 4.397211
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} 2.515601{col 30}{space 2} 1.263486{col 58}{space 4} .0392144{col 71}{space 3} 4.991987
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 3.628032{col 30}{space 2} 1.187411{col 58}{space 4} 1.300749{col 71}{space 3} 5.955316
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 4.286615{col 30}{space 2}  1.15894{col 58}{space 4} 2.015133{col 71}{space 3} 6.558096
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit Unemploy Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP LaborMember PartyMember, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1879.6871}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1847.601}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1847.4779}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1847.4779}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,070}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(13)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1847.4779}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0171}

{txt}{ralign 82:(Std. err. adjusted for {res:23} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}Exportlib {c |}{col 18}{res}{space 2} .0110019{col 30}{space 2} .0118739{col 41}{space 1}    0.93{col 50}{space 3}0.354{col 58}{space 4}-.0122706{col 71}{space 3} .0342744
{txt}{space 7}Importlib {c |}{col 18}{res}{space 2} .0411329{col 30}{space 2} .0161066{col 41}{space 1}    2.55{col 50}{space 3}0.011{col 58}{space 4} .0095645{col 71}{space 3} .0727013
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.1719354{col 30}{space 2} .1362553{col 41}{space 1}   -1.26{col 50}{space 3}0.207{col 58}{space 4}-.4389908{col 71}{space 3} .0951201
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2}  .914905{col 30}{space 2} .4841073{col 41}{space 1}    1.89{col 50}{space 3}0.059{col 58}{space 4}-.0339279{col 71}{space 3} 1.863738
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0095682{col 30}{space 2} .0128686{col 41}{space 1}   -0.74{col 50}{space 3}0.457{col 58}{space 4}-.0347901{col 71}{space 3} .0156538
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0536528{col 30}{space 2} .0321544{col 41}{space 1}   -1.67{col 50}{space 3}0.095{col 58}{space 4}-.1166742{col 71}{space 3} .0093687
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.1052193{col 30}{space 2} .0557633{col 41}{space 1}   -1.89{col 50}{space 3}0.059{col 58}{space 4}-.2145134{col 71}{space 3} .0040747
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0442493{col 30}{space 2} .1268844{col 41}{space 1}   -0.35{col 50}{space 3}0.727{col 58}{space 4}-.2929382{col 71}{space 3} .2044396
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.2045023{col 30}{space 2}  .128116{col 41}{space 1}   -1.60{col 50}{space 3}0.110{col 58}{space 4} -.455605{col 71}{space 3} .0466004
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0249282{col 30}{space 2} .0425081{col 41}{space 1}   -0.59{col 50}{space 3}0.558{col 58}{space 4}-.1082426{col 71}{space 3} .0583861
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0274303{col 30}{space 2} .0265442{col 41}{space 1}    1.03{col 50}{space 3}0.301{col 58}{space 4}-.0245953{col 71}{space 3}  .079456
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0048406{col 30}{space 2} .0057863{col 41}{space 1}   -0.84{col 50}{space 3}0.403{col 58}{space 4}-.0161815{col 71}{space 3} .0065003
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0723397{col 30}{space 2} .0536199{col 41}{space 1}    1.35{col 50}{space 3}0.177{col 58}{space 4}-.0327533{col 71}{space 3} .1774327
{txt}{space 5}LaborMember {c |}{col 18}{res}{space 2}-.1221528{col 30}{space 2} .1851293{col 41}{space 1}   -0.66{col 50}{space 3}0.509{col 58}{space 4}-.4849997{col 71}{space 3}  .240694
{txt}{space 5}PartyMember {c |}{col 18}{res}{space 2}  .057625{col 30}{space 2} .2625734{col 41}{space 1}    0.22{col 50}{space 3}0.826{col 58}{space 4}-.4570093{col 71}{space 3} .5722594
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-3.010066{col 30}{space 2} 2.187511{col 58}{space 4}-7.297508{col 71}{space 3} 1.277377
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-2.395999{col 30}{space 2} 2.213888{col 58}{space 4}-6.735139{col 71}{space 3} 1.943141
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}-1.790286{col 30}{space 2} 2.224419{col 58}{space 4}-6.150067{col 71}{space 3} 2.569494
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2}-1.524437{col 30}{space 2} 2.209299{col 58}{space 4}-5.854584{col 71}{space 3} 2.805711
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2}  -1.2168{col 30}{space 2} 2.184376{col 58}{space 4}-5.498098{col 71}{space 3} 3.064498
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2}-.8469419{col 30}{space 2}  2.13896{col 58}{space 4}-5.039227{col 71}{space 3} 3.345344
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2}-.2470871{col 30}{space 2} 2.112606{col 58}{space 4}-4.387719{col 71}{space 3} 3.893544
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} .8980475{col 30}{space 2} 2.084693{col 58}{space 4}-3.187876{col 71}{space 3} 4.983971
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 1.951774{col 30}{space 2} 2.078331{col 58}{space 4}-2.121679{col 71}{space 3} 6.025227
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. gllamm Govresp Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP LaborMember PartyMember, i(V256) link(ologit) adapt
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-2351.0652
{txt}Iteration 1:    log likelihood = {res}-2347.4913
{txt}Iteration 2:    log likelihood = {res}-2347.0298
{txt}Iteration 3:    log likelihood = {res}-2346.6309
{txt}Iteration 4:    log likelihood = {res} -2346.544
{txt}Iteration 5:    log likelihood = {res}-2346.2171
{txt}Iteration 6:    log likelihood = {res}-2346.2171


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-2346.2171{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-2346.2171{txt}  
Iteration 2:{col 16}log likelihood = {res}-2346.0172{txt}  
Iteration 3:{col 16}log likelihood = {res}-2346.0161{txt}  
Iteration 4:{col 16}log likelihood = {res}-2346.0161{txt}  
{res} 
{txt}number of level 1 units = {res}1143
{txt}number of level 2 units = {res}24
 
{txt}Condition Number = {res}4302.3286
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-2346.0161
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Govresp          {txt}{c |}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0188926{col 30}{space 2} .0055584{col 41}{space 1}    3.40{col 50}{space 3}0.001{col 58}{space 4} .0079983{col 71}{space 3} .0297869
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3377647{col 30}{space 2} .1314596{col 41}{space 1}   -2.57{col 50}{space 3}0.010{col 58}{space 4}-.5954208{col 71}{space 3}-.0801086
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5242335{col 30}{space 2} .2194596{col 41}{space 1}    2.39{col 50}{space 3}0.017{col 58}{space 4} .0941005{col 71}{space 3} .9543664
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} .0002051{col 30}{space 2} .0060508{col 41}{space 1}    0.03{col 50}{space 3}0.973{col 58}{space 4}-.0116542{col 71}{space 3} .0120645
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0531599{col 30}{space 2} .0132775{col 41}{space 1}   -4.00{col 50}{space 3}0.000{col 58}{space 4}-.0791832{col 71}{space 3}-.0271365
{txt}{space 10}Income {c |}{col 18}{res}{space 2} -.040172{col 30}{space 2} .0313704{col 41}{space 1}   -1.28{col 50}{space 3}0.200{col 58}{space 4}-.1016568{col 71}{space 3} .0213128
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0622303{col 30}{space 2} .1220347{col 41}{space 1}   -0.51{col 50}{space 3}0.610{col 58}{space 4}-.3014138{col 71}{space 3} .1769532
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1023627{col 30}{space 2} .1061943{col 41}{space 1}   -0.96{col 50}{space 3}0.335{col 58}{space 4}-.3104998{col 71}{space 3} .1057744
{txt}{space 7}Education {c |}{col 18}{res}{space 2}  .003739{col 30}{space 2} .0306466{col 41}{space 1}    0.12{col 50}{space 3}0.903{col 58}{space 4}-.0563272{col 71}{space 3} .0638053
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0396808{col 30}{space 2} .0251436{col 41}{space 1}   -1.58{col 50}{space 3}0.115{col 58}{space 4}-.0889613{col 71}{space 3} .0095996
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0018745{col 30}{space 2} .0048074{col 41}{space 1}    0.39{col 50}{space 3}0.697{col 58}{space 4}-.0075478{col 71}{space 3} .0112969
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3921288{col 30}{space 2} .0243118{col 41}{space 1}   16.13{col 50}{space 3}0.000{col 58}{space 4} .3444787{col 71}{space 3}  .439779
{txt}{space 5}LaborMember {c |}{col 18}{res}{space 2}-.1568377{col 30}{space 2} .1767958{col 41}{space 1}   -0.89{col 50}{space 3}0.375{col 58}{space 4} -.503351{col 71}{space 3} .1896756
{txt}{space 5}PartyMember {c |}{col 18}{res}{space 2} .1246662{col 30}{space 2}  .188898{col 41}{space 1}    0.66{col 50}{space 3}0.509{col 58}{space 4}-.2455671{col 71}{space 3} .4948995
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} -1.73001{col 30}{space 2} 1.140911{col 41}{space 1}   -1.52{col 50}{space 3}0.129{col 58}{space 4}-3.966156{col 71}{space 3} .5061348
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.6241681{col 30}{space 2} 1.135322{col 41}{space 1}   -0.55{col 50}{space 3}0.582{col 58}{space 4}-2.849359{col 71}{space 3} 1.601023
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .3029656{col 30}{space 2}  1.13445{col 41}{space 1}    0.27{col 50}{space 3}0.789{col 58}{space 4}-1.920516{col 71}{space 3} 2.526447
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .9826542{col 30}{space 2} 1.134792{col 41}{space 1}    0.87{col 50}{space 3}0.387{col 58}{space 4}-1.241498{col 71}{space 3} 3.206806
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}    1.527{col 30}{space 2} 1.135337{col 41}{space 1}    1.34{col 50}{space 3}0.179{col 58}{space 4}-.6982197{col 71}{space 3}  3.75222
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.026471{col 30}{space 2} 1.135901{col 41}{space 1}    1.78{col 50}{space 3}0.074{col 58}{space 4}-.1998546{col 71}{space 3} 4.252797
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.659352{col 30}{space 2} 1.136926{col 41}{space 1}    2.34{col 50}{space 3}0.019{col 58}{space 4} .4310174{col 71}{space 3} 4.887687
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 3.775507{col 30}{space 2} 1.139233{col 41}{space 1}    3.31{col 50}{space 3}0.001{col 58}{space 4} 1.542651{col 71}{space 3} 6.008363
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 4.433764{col 30}{space 2} 1.141052{col 41}{space 1}    3.89{col 50}{space 3}0.000{col 58}{space 4} 2.197342{col 71}{space 3} 6.670185
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.02175615 (.02940473)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. gllamm Unemploy Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP LaborMember PartyMember, i(V256) link(ologit) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-1837.9402
{txt}Iteration 1:    log likelihood = {res}-1836.2157
{txt}Iteration 2:    log likelihood = {res}-1836.1555
{txt}Iteration 3:    log likelihood = {res}-1836.1178
{txt}Iteration 4:    log likelihood = {res} -1836.081
{txt}Iteration 5:    log likelihood = {res} -1836.045
{txt}Iteration 6:    log likelihood = {res}-1836.0441


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-1836.0441{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-1836.0436{txt}  
Iteration 2:{col 16}log likelihood = {res} -1835.968{txt}  
Iteration 3:{col 16}log likelihood = {res}-1835.9673{txt}  
Iteration 4:{col 16}log likelihood = {res}-1835.9673{txt}  
{res} 
{txt}number of level 1 units = {res}1070
{txt}number of level 2 units = {res}23
 
{txt}Condition Number = {res}4015.5076
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-1835.9673
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Unemploy         {txt}{c |}
{space 8}Tradelib {c |}{col 18}{res}{space 2}  .022216{col 30}{space 2} .0104085{col 41}{space 1}    2.13{col 50}{space 3}0.033{col 58}{space 4} .0018157{col 71}{space 3} .0426163
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.2093791{col 30}{space 2} .1422265{col 41}{space 1}   -1.47{col 50}{space 3}0.141{col 58}{space 4}-.4881379{col 71}{space 3} .0693796
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5478973{col 30}{space 2} .3952532{col 41}{space 1}    1.39{col 50}{space 3}0.166{col 58}{space 4}-.2267847{col 71}{space 3} 1.322579
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0169547{col 30}{space 2}  .011435{col 41}{space 1}   -1.48{col 50}{space 3}0.138{col 58}{space 4}-.0393669{col 71}{space 3} .0054576
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0261829{col 30}{space 2} .0248545{col 41}{space 1}   -1.05{col 50}{space 3}0.292{col 58}{space 4}-.0748967{col 71}{space 3}  .022531
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0747319{col 30}{space 2} .0344915{col 41}{space 1}   -2.17{col 50}{space 3}0.030{col 58}{space 4} -.142334{col 71}{space 3}-.0071298
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0696479{col 30}{space 2} .1319789{col 41}{space 1}   -0.53{col 50}{space 3}0.598{col 58}{space 4}-.3283217{col 71}{space 3} .1890259
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1952134{col 30}{space 2} .1136997{col 41}{space 1}   -1.72{col 50}{space 3}0.086{col 58}{space 4}-.4180606{col 71}{space 3} .0276339
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0421292{col 30}{space 2} .0332103{col 41}{space 1}   -1.27{col 50}{space 3}0.205{col 58}{space 4}-.1072203{col 71}{space 3} .0229619
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0289327{col 30}{space 2} .0257313{col 41}{space 1}    1.12{col 50}{space 3}0.261{col 58}{space 4}-.0214998{col 71}{space 3} .0793651
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0045491{col 30}{space 2} .0050944{col 41}{space 1}   -0.89{col 50}{space 3}0.372{col 58}{space 4}-.0145339{col 71}{space 3} .0054358
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0766122{col 30}{space 2}   .02202{col 41}{space 1}    3.48{col 50}{space 3}0.001{col 58}{space 4} .0334537{col 71}{space 3} .1197707
{txt}{space 5}LaborMember {c |}{col 18}{res}{space 2}-.0324856{col 30}{space 2} .1886013{col 41}{space 1}   -0.17{col 50}{space 3}0.863{col 58}{space 4}-.4021373{col 71}{space 3} .3371662
{txt}{space 5}PartyMember {c |}{col 18}{res}{space 2} -.008966{col 30}{space 2} .1947287{col 41}{space 1}   -0.05{col 50}{space 3}0.963{col 58}{space 4}-.3906274{col 71}{space 3} .3726953
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-3.509574{col 30}{space 2} 1.992167{col 41}{space 1}   -1.76{col 50}{space 3}0.078{col 58}{space 4}-7.414149{col 71}{space 3} .3950007
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.888416{col 30}{space 2} 1.986798{col 41}{space 1}   -1.45{col 50}{space 3}0.146{col 58}{space 4}-6.782468{col 71}{space 3} 1.005636
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.269989{col 30}{space 2} 1.984272{col 41}{space 1}   -1.14{col 50}{space 3}0.253{col 58}{space 4}-6.159091{col 71}{space 3} 1.619113
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.998424{col 30}{space 2} 1.983597{col 41}{space 1}   -1.01{col 50}{space 3}0.314{col 58}{space 4}-5.886203{col 71}{space 3} 1.889356
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.684311{col 30}{space 2} 1.982867{col 41}{space 1}   -0.85{col 50}{space 3}0.396{col 58}{space 4}-5.570659{col 71}{space 3} 2.202038
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.303866{col 30}{space 2} 1.981998{col 41}{space 1}   -0.66{col 50}{space 3}0.511{col 58}{space 4}-5.188511{col 71}{space 3} 2.580778
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.6827608{col 30}{space 2} 1.981086{col 41}{space 1}   -0.34{col 50}{space 3}0.730{col 58}{space 4}-4.565617{col 71}{space 3} 3.200096
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .5028118{col 30}{space 2} 1.980479{col 41}{space 1}    0.25{col 50}{space 3}0.800{col 58}{space 4}-3.378857{col 71}{space 3}  4.38448
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.594478{col 30}{space 2} 1.980747{col 41}{space 1}    0.80{col 50}{space 3}0.421{col 58}{space 4}-2.287714{col 71}{space 3}  5.47667
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.21551113 (.08990497)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. gllamm Govresp Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP LaborMember PartyMember, i(V256) link(ologit) adapt
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-2351.0455
{txt}Iteration 1:    log likelihood = {res}-2347.1504
{txt}Iteration 2:    log likelihood = {res} -2346.243
{txt}Iteration 3:    log likelihood = {res} -2346.243


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res} -2346.243{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res} -2346.243{txt}  
Iteration 2:{col 16}log likelihood = {res}-2345.9824{txt}  
Iteration 3:{col 16}log likelihood = {res}-2345.9749{txt}  
Iteration 4:{col 16}log likelihood = {res}-2345.9749{txt}  
{res} 
{txt}number of level 1 units = {res}1143
{txt}number of level 2 units = {res}24
 
{txt}Condition Number = {res}4083.7209
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-2345.9749
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Govresp          {txt}{c |}
{space 7}Exportlib {c |}{col 18}{res}{space 2} .0200569{col 30}{space 2} .0069261{col 41}{space 1}    2.90{col 50}{space 3}0.004{col 58}{space 4}  .006482{col 71}{space 3} .0336318
{txt}{space 7}Importlib {c |}{col 18}{res}{space 2} .0173731{col 30}{space 2} .0077404{col 41}{space 1}    2.24{col 50}{space 3}0.025{col 58}{space 4} .0022023{col 71}{space 3}  .032544
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3365371{col 30}{space 2} .1315614{col 41}{space 1}   -2.56{col 50}{space 3}0.011{col 58}{space 4}-.5943927{col 71}{space 3}-.0786816
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5157533{col 30}{space 2} .2226362{col 41}{space 1}    2.32{col 50}{space 3}0.021{col 58}{space 4} .0793944{col 71}{space 3} .9521122
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0002822{col 30}{space 2} .0063099{col 41}{space 1}   -0.04{col 50}{space 3}0.964{col 58}{space 4}-.0126495{col 71}{space 3}  .012085
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0499749{col 30}{space 2} .0175026{col 41}{space 1}   -2.86{col 50}{space 3}0.004{col 58}{space 4}-.0842795{col 71}{space 3}-.0156704
{txt}{space 10}Income {c |}{col 18}{res}{space 2} -.039926{col 30}{space 2} .0313846{col 41}{space 1}   -1.27{col 50}{space 3}0.203{col 58}{space 4}-.1014386{col 71}{space 3} .0215867
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0641758{col 30}{space 2} .1222451{col 41}{space 1}   -0.52{col 50}{space 3}0.600{col 58}{space 4}-.3037718{col 71}{space 3} .1754202
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1028026{col 30}{space 2} .1062145{col 41}{space 1}   -0.97{col 50}{space 3}0.333{col 58}{space 4}-.3109793{col 71}{space 3}  .105374
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0031715{col 30}{space 2} .0307164{col 41}{space 1}    0.10{col 50}{space 3}0.918{col 58}{space 4}-.0570315{col 71}{space 3} .0633744
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0400687{col 30}{space 2} .0251728{col 41}{space 1}   -1.59{col 50}{space 3}0.111{col 58}{space 4}-.0894065{col 71}{space 3}  .009269
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0018988{col 30}{space 2} .0048085{col 41}{space 1}    0.39{col 50}{space 3}0.693{col 58}{space 4}-.0075257{col 71}{space 3} .0113233
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3922132{col 30}{space 2} .0243239{col 41}{space 1}   16.12{col 50}{space 3}0.000{col 58}{space 4} .3445392{col 71}{space 3} .4398871
{txt}{space 5}LaborMember {c |}{col 18}{res}{space 2} -.149894{col 30}{space 2}  .178647{col 41}{space 1}   -0.84{col 50}{space 3}0.401{col 58}{space 4}-.5000356{col 71}{space 3} .2002477
{txt}{space 5}PartyMember {c |}{col 18}{res}{space 2} .1187731{col 30}{space 2} .1901116{col 41}{space 1}    0.62{col 50}{space 3}0.532{col 58}{space 4}-.2538389{col 71}{space 3}  .491385
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.650489{col 30}{space 2} 1.182248{col 41}{space 1}   -1.40{col 50}{space 3}0.163{col 58}{space 4}-3.967652{col 71}{space 3} .6666738
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.5443407{col 30}{space 2} 1.177162{col 41}{space 1}   -0.46{col 50}{space 3}0.644{col 58}{space 4}-2.851536{col 71}{space 3} 1.762854
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .3835133{col 30}{space 2} 1.176952{col 41}{space 1}    0.33{col 50}{space 3}0.745{col 58}{space 4} -1.92327{col 71}{space 3} 2.690297
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.063739{col 30}{space 2} 1.177762{col 41}{space 1}    0.90{col 50}{space 3}0.366{col 58}{space 4}-1.244632{col 71}{space 3}  3.37211
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.608325{col 30}{space 2} 1.178528{col 41}{space 1}    1.36{col 50}{space 3}0.172{col 58}{space 4}-.7015474{col 71}{space 3} 3.918198
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.107863{col 30}{space 2} 1.179163{col 41}{space 1}    1.79{col 50}{space 3}0.074{col 58}{space 4}-.2032534{col 71}{space 3} 4.418979
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.740562{col 30}{space 2} 1.180045{col 41}{space 1}    2.32{col 50}{space 3}0.020{col 58}{space 4} .4277164{col 71}{space 3} 5.053408
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}  3.85644{col 30}{space 2} 1.182105{col 41}{space 1}    3.26{col 50}{space 3}0.001{col 58}{space 4} 1.539556{col 71}{space 3} 6.173323
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 4.514658{col 30}{space 2} 1.183833{col 41}{space 1}    3.81{col 50}{space 3}0.000{col 58}{space 4} 2.194389{col 71}{space 3} 6.834928
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.02284159 (.03010404)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. gllamm Unemploy Exportlib Importlib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP LaborMember PartyMember, i(V256) link(ologit) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-1836.6864
{txt}Iteration 1:    log likelihood = {res}-1834.8401
{txt}Iteration 2:    log likelihood = {res}-1834.6939
{txt}Iteration 3:    log likelihood = {res}  -1834.69
{txt}Iteration 4:    log likelihood = {res}-1834.6668
{txt}Iteration 5:    log likelihood = {res}-1834.6451
{txt}Iteration 6:    log likelihood = {res}-1834.6388
{txt}Iteration 7:    log likelihood = {res}-1834.6388


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-1834.6388{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-1834.6388{txt}  
Iteration 2:{col 16}log likelihood = {res}-1834.6041{txt}  
Iteration 3:{col 16}log likelihood = {res} -1834.604{txt}  
{res} 
{txt}number of level 1 units = {res}1070
{txt}number of level 2 units = {res}23
 
{txt}Condition Number = {res}3871.8963
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-1834.604
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Unemploy         {txt}{c |}
{space 7}Exportlib {c |}{col 18}{res}{space 2} .0092487{col 30}{space 2} .0123672{col 41}{space 1}    0.75{col 50}{space 3}0.455{col 58}{space 4}-.0149906{col 71}{space 3}  .033488
{txt}{space 7}Importlib {c |}{col 18}{res}{space 2} .0366636{col 30}{space 2} .0128008{col 41}{space 1}    2.86{col 50}{space 3}0.004{col 58}{space 4} .0115744{col 71}{space 3} .0617528
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.2162901{col 30}{space 2} .1422132{col 41}{space 1}   -1.52{col 50}{space 3}0.128{col 58}{space 4}-.4950229{col 71}{space 3} .0624427
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .6583747{col 30}{space 2} .3755657{col 41}{space 1}    1.75{col 50}{space 3}0.080{col 58}{space 4}-.0777205{col 71}{space 3}  1.39447
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0104212{col 30}{space 2}  .011308{col 41}{space 1}   -0.92{col 50}{space 3}0.357{col 58}{space 4}-.0325844{col 71}{space 3}  .011742
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0557848{col 30}{space 2} .0290806{col 41}{space 1}   -1.92{col 50}{space 3}0.055{col 58}{space 4}-.1127817{col 71}{space 3}  .001212
{txt}{space 10}Income {c |}{col 18}{res}{space 2} -.076849{col 30}{space 2} .0345055{col 41}{space 1}   -2.23{col 50}{space 3}0.026{col 58}{space 4}-.1444785{col 71}{space 3}-.0092195
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0627488{col 30}{space 2} .1320068{col 41}{space 1}   -0.48{col 50}{space 3}0.635{col 58}{space 4}-.3214774{col 71}{space 3} .1959797
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1955657{col 30}{space 2} .1136676{col 41}{space 1}   -1.72{col 50}{space 3}0.085{col 58}{space 4}  -.41835{col 71}{space 3} .0272187
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0405811{col 30}{space 2} .0331911{col 41}{space 1}   -1.22{col 50}{space 3}0.221{col 58}{space 4}-.1056345{col 71}{space 3} .0244724
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0293152{col 30}{space 2} .0257328{col 41}{space 1}    1.14{col 50}{space 3}0.255{col 58}{space 4}-.0211202{col 71}{space 3} .0797506
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0047811{col 30}{space 2} .0050954{col 41}{space 1}   -0.94{col 50}{space 3}0.348{col 58}{space 4}-.0147678{col 71}{space 3} .0052057
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0759567{col 30}{space 2} .0219834{col 41}{space 1}    3.46{col 50}{space 3}0.001{col 58}{space 4}   .03287{col 71}{space 3} .1190434
{txt}{space 5}LaborMember {c |}{col 18}{res}{space 2}-.0563084{col 30}{space 2} .1889015{col 41}{space 1}   -0.30{col 50}{space 3}0.766{col 58}{space 4}-.4265487{col 71}{space 3} .3139318
{txt}{space 5}PartyMember {c |}{col 18}{res}{space 2} .0115496{col 30}{space 2} .1952057{col 41}{space 1}    0.06{col 50}{space 3}0.953{col 58}{space 4}-.3710465{col 71}{space 3} .3941457
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-4.149242{col 30}{space 2} 1.926624{col 41}{space 1}   -2.15{col 50}{space 3}0.031{col 58}{space 4}-7.925356{col 71}{space 3}-.3731278
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-3.527823{col 30}{space 2} 1.920957{col 41}{space 1}   -1.84{col 50}{space 3}0.066{col 58}{space 4}-7.292829{col 71}{space 3} .2371822
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.908665{col 30}{space 2} 1.918108{col 41}{space 1}   -1.52{col 50}{space 3}0.129{col 58}{space 4}-6.668089{col 71}{space 3} .8507579
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.636846{col 30}{space 2} 1.917311{col 41}{space 1}   -1.38{col 50}{space 3}0.169{col 58}{space 4}-6.394708{col 71}{space 3} 1.121015
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} -2.32266{col 30}{space 2} 1.916476{col 41}{space 1}   -1.21{col 50}{space 3}0.226{col 58}{space 4}-6.078884{col 71}{space 3} 1.433565
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.942045{col 30}{space 2} 1.915452{col 41}{space 1}   -1.01{col 50}{space 3}0.311{col 58}{space 4}-5.696262{col 71}{space 3} 1.812171
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.320846{col 30}{space 2} 1.914303{col 41}{space 1}   -0.69{col 50}{space 3}0.490{col 58}{space 4}-5.072811{col 71}{space 3} 2.431118
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.1352088{col 30}{space 2} 1.913278{col 41}{space 1}   -0.07{col 50}{space 3}0.944{col 58}{space 4}-3.885165{col 71}{space 3} 3.614747
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .9550805{col 30}{space 2} 1.913373{col 41}{space 1}    0.50{col 50}{space 3}0.618{col 58}{space 4}-2.795061{col 71}{space 3} 4.705222
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.17764353 (.07926163)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. 
. **Trade as Lagged Variable and Economic Shocks, Table A12 
. 
. gen Tradechange= (Tradelib-tradelib)/tradelib
{txt}
{com}. label variable Tradechange "Trade Change Rate" 
{txt}
{com}. 
. 
. ologit Govresp tradelib Tradechange Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2534.9073}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2354.4939}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2347.9829}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2347.9622}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2347.9622}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,144}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:700.53}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2347.9622}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0737}

{txt}{ralign 82:(Std. err. adjusted for {res:24} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}tradelib {c |}{col 18}{res}{space 2} .0209065{col 30}{space 2} .0047985{col 41}{space 1}    4.36{col 50}{space 3}0.000{col 58}{space 4} .0115015{col 71}{space 3} .0303114
{txt}{space 5}Tradechange {c |}{col 18}{res}{space 2} .6388178{col 30}{space 2} .2927478{col 41}{space 1}    2.18{col 50}{space 3}0.029{col 58}{space 4} .0650426{col 71}{space 3} 1.212593
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3174529{col 30}{space 2} .1434838{col 41}{space 1}   -2.21{col 50}{space 3}0.027{col 58}{space 4}-.5986761{col 71}{space 3}-.0362298
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5548615{col 30}{space 2} .2159034{col 41}{space 1}    2.57{col 50}{space 3}0.010{col 58}{space 4} .1316986{col 71}{space 3} .9780244
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} .0002256{col 30}{space 2} .0031201{col 41}{space 1}    0.07{col 50}{space 3}0.942{col 58}{space 4}-.0058897{col 71}{space 3} .0063409
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0540169{col 30}{space 2} .0134612{col 41}{space 1}   -4.01{col 50}{space 3}0.000{col 58}{space 4}-.0804005{col 71}{space 3}-.0276334
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0528691{col 30}{space 2} .0342793{col 41}{space 1}   -1.54{col 50}{space 3}0.123{col 58}{space 4}-.1200553{col 71}{space 3} .0143172
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0711061{col 30}{space 2} .1242699{col 41}{space 1}   -0.57{col 50}{space 3}0.567{col 58}{space 4}-.3146706{col 71}{space 3} .1724585
{txt}{space 10}Female {c |}{col 18}{res}{space 2} -.114232{col 30}{space 2} .0886036{col 41}{space 1}   -1.29{col 50}{space 3}0.197{col 58}{space 4}-.2878919{col 71}{space 3} .0594279
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0035133{col 30}{space 2} .0422576{col 41}{space 1}    0.08{col 50}{space 3}0.934{col 58}{space 4}-.0793101{col 71}{space 3} .0863368
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0366083{col 30}{space 2} .0251887{col 41}{space 1}   -1.45{col 50}{space 3}0.146{col 58}{space 4}-.0859772{col 71}{space 3} .0127605
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0020248{col 30}{space 2} .0046044{col 41}{space 1}    0.44{col 50}{space 3}0.660{col 58}{space 4}-.0069996{col 71}{space 3} .0110492
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3919952{col 30}{space 2} .0292151{col 41}{space 1}   13.42{col 50}{space 3}0.000{col 58}{space 4} .3347347{col 71}{space 3} .4492557
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-1.598112{col 30}{space 2} 1.125006{col 58}{space 4}-3.803084{col 71}{space 3} .6068595
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-.4931198{col 30}{space 2} 1.191732{col 58}{space 4}-2.828872{col 71}{space 3} 1.842632
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2} .4399865{col 30}{space 2} 1.159564{col 58}{space 4}-1.832717{col 71}{space 3}  2.71269
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2} 1.115925{col 30}{space 2} 1.131324{col 58}{space 4} -1.10143{col 71}{space 3}  3.33328
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2} 1.657525{col 30}{space 2} 1.103487{col 58}{space 4}-.5052689{col 71}{space 3} 3.820319
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2}  2.15534{col 30}{space 2} 1.123227{col 58}{space 4} -.046144{col 71}{space 3} 4.356824
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2}  2.78611{col 30}{space 2} 1.113704{col 58}{space 4} .6032906{col 71}{space 3} 4.968929
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 3.899096{col 30}{space 2} 1.062318{col 58}{space 4} 1.816991{col 71}{space 3}   5.9812
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 4.555503{col 30}{space 2} 1.045454{col 58}{space 4} 2.506451{col 71}{space 3} 6.604555
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit Unemploy tradelib Tradechange Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, cluster(V256)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1882.9868}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1856.3331}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1856.2489}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1856.2489}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,071}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:57.53}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1856.2489}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0142}

{txt}{ralign 82:(Std. err. adjusted for {res:23} clusters in {res:V256})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}tradelib {c |}{col 18}{res}{space 2} .0275767{col 30}{space 2} .0102975{col 41}{space 1}    2.68{col 50}{space 3}0.007{col 58}{space 4} .0073939{col 71}{space 3} .0477594
{txt}{space 5}Tradechange {c |}{col 18}{res}{space 2} .5253477{col 30}{space 2} .5893671{col 41}{space 1}    0.89{col 50}{space 3}0.373{col 58}{space 4}-.6297906{col 71}{space 3} 1.680486
{txt}{space 9}Private {c |}{col 18}{res}{space 2} -.115602{col 30}{space 2} .1287993{col 41}{space 1}   -0.90{col 50}{space 3}0.369{col 58}{space 4}-.3680441{col 71}{space 3}   .13684
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .8681604{col 30}{space 2} .5083781{col 41}{space 1}    1.71{col 50}{space 3}0.088{col 58}{space 4}-.1282424{col 71}{space 3} 1.864563
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0167623{col 30}{space 2} .0116709{col 41}{space 1}   -1.44{col 50}{space 3}0.151{col 58}{space 4}-.0396369{col 71}{space 3} .0061123
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0198071{col 30}{space 2} .0133426{col 41}{space 1}   -1.48{col 50}{space 3}0.138{col 58}{space 4}-.0459582{col 71}{space 3} .0063439
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.1045848{col 30}{space 2} .0607454{col 41}{space 1}   -1.72{col 50}{space 3}0.085{col 58}{space 4}-.2236435{col 71}{space 3} .0144739
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0689866{col 30}{space 2} .1288009{col 41}{space 1}   -0.54{col 50}{space 3}0.592{col 58}{space 4}-.3214318{col 71}{space 3} .1834586
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.2150673{col 30}{space 2} .1278319{col 41}{space 1}   -1.68{col 50}{space 3}0.092{col 58}{space 4}-.4656132{col 71}{space 3} .0354786
{txt}{space 7}Education {c |}{col 18}{res}{space 2} -.031213{col 30}{space 2} .0413865{col 41}{space 1}   -0.75{col 50}{space 3}0.451{col 58}{space 4}-.1123291{col 71}{space 3} .0499032
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0243871{col 30}{space 2} .0262758{col 41}{space 1}    0.93{col 50}{space 3}0.353{col 58}{space 4}-.0271124{col 71}{space 3} .0758867
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0035073{col 30}{space 2} .0052499{col 41}{space 1}   -0.67{col 50}{space 3}0.504{col 58}{space 4}-.0137969{col 71}{space 3} .0067824
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0739582{col 30}{space 2} .0538689{col 41}{space 1}    1.37{col 50}{space 3}0.170{col 58}{space 4}-.0316229{col 71}{space 3} .1795394
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}/cut1 {c |}{col 18}{res}{space 2}-1.843585{col 30}{space 2} 2.222567{col 58}{space 4}-6.199736{col 71}{space 3} 2.512566
{txt}{space 11}/cut2 {c |}{col 18}{res}{space 2}-1.231717{col 30}{space 2} 2.302816{col 58}{space 4}-5.745154{col 71}{space 3}  3.28172
{txt}{space 11}/cut3 {c |}{col 18}{res}{space 2}-.6307584{col 30}{space 2} 2.313292{col 58}{space 4}-5.164727{col 71}{space 3}  3.90321
{txt}{space 11}/cut4 {c |}{col 18}{res}{space 2}-.3670135{col 30}{space 2} 2.283666{col 58}{space 4}-4.842918{col 71}{space 3} 4.108891
{txt}{space 11}/cut5 {c |}{col 18}{res}{space 2} -.061239{col 30}{space 2} 2.259483{col 58}{space 4}-4.489745{col 71}{space 3} 4.367267
{txt}{space 11}/cut6 {c |}{col 18}{res}{space 2} .3138687{col 30}{space 2} 2.229678{col 58}{space 4} -4.05622{col 71}{space 3} 4.683957
{txt}{space 11}/cut7 {c |}{col 18}{res}{space 2} .9065284{col 30}{space 2} 2.209173{col 58}{space 4}-3.423371{col 71}{space 3} 5.236428
{txt}{space 11}/cut8 {c |}{col 18}{res}{space 2} 2.039597{col 30}{space 2} 2.194733{col 58}{space 4}   -2.262{col 71}{space 3} 6.341194
{txt}{space 11}/cut9 {c |}{col 18}{res}{space 2} 3.087791{col 30}{space 2} 2.202338{col 58}{space 4}-1.228713{col 71}{space 3} 7.404294
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. gllamm Govresp tradelib Tradechange Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256) link(ologit) adapt
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res} -2353.202
{txt}Iteration 1:    log likelihood = {res}-2349.9393
{txt}Iteration 2:    log likelihood = {res}-2348.0469
{txt}Iteration 3:    log likelihood = {res}-2347.9481
{txt}Iteration 4:    log likelihood = {res}-2347.8925
{txt}Iteration 5:    log likelihood = {res}-2347.8925


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-2347.8925{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-2347.8925{txt}  
Iteration 2:{col 16}log likelihood = {res}-2347.7305{txt}  
Iteration 3:{col 16}log likelihood = {res}-2347.7302{txt}  
Iteration 4:{col 16}log likelihood = {res}-2347.7302{txt}  
{res} 
{txt}number of level 1 units = {res}1144
{txt}number of level 2 units = {res}24
 
{txt}Condition Number = {res}4361.5984
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-2347.7302
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Govresp          {txt}{c |}
{space 8}tradelib {c |}{col 18}{res}{space 2} .0205998{col 30}{space 2} .0057839{col 41}{space 1}    3.56{col 50}{space 3}0.000{col 58}{space 4} .0092635{col 71}{space 3} .0319361
{txt}{space 5}Tradechange {c |}{col 18}{res}{space 2}  .625744{col 30}{space 2} .3337278{col 41}{space 1}    1.88{col 50}{space 3}0.061{col 58}{space 4}-.0283504{col 71}{space 3} 1.279838
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3264705{col 30}{space 2} .1293168{col 41}{space 1}   -2.52{col 50}{space 3}0.012{col 58}{space 4}-.5799268{col 71}{space 3}-.0730142
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5658327{col 30}{space 2} .2137434{col 41}{space 1}    2.65{col 50}{space 3}0.008{col 58}{space 4} .1469033{col 71}{space 3} .9847621
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} .0008053{col 30}{space 2} .0058563{col 41}{space 1}    0.14{col 50}{space 3}0.891{col 58}{space 4}-.0106729{col 71}{space 3} .0122835
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0527066{col 30}{space 2} .0128436{col 41}{space 1}   -4.10{col 50}{space 3}0.000{col 58}{space 4}-.0778795{col 71}{space 3}-.0275337
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0476382{col 30}{space 2} .0317311{col 41}{space 1}   -1.50{col 50}{space 3}0.133{col 58}{space 4}-.1098299{col 71}{space 3} .0145536
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0730938{col 30}{space 2}  .120782{col 41}{space 1}   -0.61{col 50}{space 3}0.545{col 58}{space 4}-.3098221{col 71}{space 3} .1636345
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1140941{col 30}{space 2} .1060186{col 41}{space 1}   -1.08{col 50}{space 3}0.282{col 58}{space 4}-.3218867{col 71}{space 3} .0936985
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0035244{col 30}{space 2}  .030473{col 41}{space 1}    0.12{col 50}{space 3}0.908{col 58}{space 4}-.0562016{col 71}{space 3} .0632504
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} -.039083{col 30}{space 2} .0250983{col 41}{space 1}   -1.56{col 50}{space 3}0.119{col 58}{space 4}-.0882748{col 71}{space 3} .0101088
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0020206{col 30}{space 2} .0047689{col 41}{space 1}    0.42{col 50}{space 3}0.672{col 58}{space 4}-.0073263{col 71}{space 3} .0113675
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3897401{col 30}{space 2} .0241459{col 41}{space 1}   16.14{col 50}{space 3}0.000{col 58}{space 4}  .342415{col 71}{space 3} .4370653
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.508295{col 30}{space 2}  1.11196{col 41}{space 1}   -1.36{col 50}{space 3}0.175{col 58}{space 4}-3.687696{col 71}{space 3} .6711064
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.4015642{col 30}{space 2} 1.106277{col 41}{space 1}   -0.36{col 50}{space 3}0.717{col 58}{space 4}-2.569827{col 71}{space 3} 1.766699
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .5331092{col 30}{space 2} 1.105595{col 41}{space 1}    0.48{col 50}{space 3}0.630{col 58}{space 4}-1.633818{col 71}{space 3} 2.700036
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.210529{col 30}{space 2} 1.106093{col 41}{space 1}    1.09{col 50}{space 3}0.274{col 58}{space 4}-.9573739{col 71}{space 3} 3.378432
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.754062{col 30}{space 2} 1.106829{col 41}{space 1}    1.58{col 50}{space 3}0.113{col 58}{space 4} -.415284{col 71}{space 3} 3.923408
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.253519{col 30}{space 2} 1.107534{col 41}{space 1}    2.03{col 50}{space 3}0.042{col 58}{space 4}  .082792{col 71}{space 3} 4.424246
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.886293{col 30}{space 2} 1.108594{col 41}{space 1}    2.60{col 50}{space 3}0.009{col 58}{space 4} .7134887{col 71}{space 3} 5.059097
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 4.001553{col 30}{space 2}  1.11087{col 41}{space 1}    3.60{col 50}{space 3}0.000{col 58}{space 4} 1.824288{col 71}{space 3} 6.178819
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 4.658062{col 30}{space 2} 1.112587{col 41}{space 1}    4.19{col 50}{space 3}0.000{col 58}{space 4} 2.477431{col 71}{space 3} 6.838693
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.01384104 (.02484711)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. gllamm Unemploy tradelib Tradechange Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age EqualityP, i(V256) link(ologit) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-1840.6215
{txt}Iteration 1:    log likelihood = {res}-1838.8934
{txt}Iteration 2:    log likelihood = {res}-1838.7015
{txt}Iteration 3:    log likelihood = {res}-1838.6777
{txt}Iteration 4:    log likelihood = {res}-1838.5046
{txt}Iteration 5:    log likelihood = {res}-1838.4488
{txt}Iteration 6:    log likelihood = {res}-1838.4488


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-1838.4488{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-1838.4488{txt}  
Iteration 2:{col 16}log likelihood = {res}-1838.4319{txt}  
Iteration 3:{col 16}log likelihood = {res}-1838.4319{txt}  
{res} 
{txt}number of level 1 units = {res}1071
{txt}number of level 2 units = {res}23
 
{txt}Condition Number = {res}4035.0956
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-1838.4319
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Unemploy         {txt}{c |}
{space 8}tradelib {c |}{col 18}{res}{space 2} .0253544{col 30}{space 2}  .011258{col 41}{space 1}    2.25{col 50}{space 3}0.024{col 58}{space 4} .0032891{col 71}{space 3} .0474198
{txt}{space 5}Tradechange {c |}{col 18}{res}{space 2} .2752614{col 30}{space 2} .5967516{col 41}{space 1}    0.46{col 50}{space 3}0.645{col 58}{space 4}-.8943503{col 71}{space 3} 1.444873
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.1987789{col 30}{space 2} .1400437{col 41}{space 1}   -1.42{col 50}{space 3}0.156{col 58}{space 4}-.4732595{col 71}{space 3} .0757016
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5789617{col 30}{space 2} .3994421{col 41}{space 1}    1.45{col 50}{space 3}0.147{col 58}{space 4}-.2039305{col 71}{space 3} 1.361854
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0183169{col 30}{space 2} .0114847{col 41}{space 1}   -1.59{col 50}{space 3}0.111{col 58}{space 4}-.0408264{col 71}{space 3} .0041927
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0293687{col 30}{space 2} .0253051{col 41}{space 1}   -1.16{col 50}{space 3}0.246{col 58}{space 4}-.0789658{col 71}{space 3} .0202284
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0757512{col 30}{space 2} .0343899{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4}-.1431542{col 71}{space 3}-.0083483
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0670452{col 30}{space 2} .1312137{col 41}{space 1}   -0.51{col 50}{space 3}0.609{col 58}{space 4}-.3242193{col 71}{space 3} .1901288
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1985698{col 30}{space 2}  .113561{col 41}{space 1}   -1.75{col 50}{space 3}0.080{col 58}{space 4}-.4211453{col 71}{space 3} .0240057
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0440169{col 30}{space 2} .0330354{col 41}{space 1}   -1.33{col 50}{space 3}0.183{col 58}{space 4}-.1087651{col 71}{space 3} .0207313
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0298413{col 30}{space 2} .0256826{col 41}{space 1}    1.16{col 50}{space 3}0.245{col 58}{space 4}-.0204956{col 71}{space 3} .0801783
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0044002{col 30}{space 2} .0050741{col 41}{space 1}   -0.87{col 50}{space 3}0.386{col 58}{space 4}-.0143453{col 71}{space 3} .0055449
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0774609{col 30}{space 2} .0220149{col 41}{space 1}    3.52{col 50}{space 3}0.000{col 58}{space 4} .0343124{col 71}{space 3} .1206093
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-3.480356{col 30}{space 2} 2.023835{col 41}{space 1}   -1.72{col 50}{space 3}0.085{col 58}{space 4}-7.447001{col 71}{space 3} .4862882
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.859073{col 30}{space 2} 2.018668{col 41}{space 1}   -1.42{col 50}{space 3}0.157{col 58}{space 4}-6.815589{col 71}{space 3} 1.097443
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.240556{col 30}{space 2} 2.016404{col 41}{space 1}   -1.11{col 50}{space 3}0.266{col 58}{space 4}-6.192635{col 71}{space 3} 1.711523
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.968917{col 30}{space 2} 2.015827{col 41}{space 1}   -0.98{col 50}{space 3}0.329{col 58}{space 4}-5.919865{col 71}{space 3}  1.98203
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.654693{col 30}{space 2} 2.015174{col 41}{space 1}   -0.82{col 50}{space 3}0.412{col 58}{space 4}-5.604362{col 71}{space 3} 2.294975
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.265188{col 30}{space 2} 2.014356{col 41}{space 1}   -0.63{col 50}{space 3}0.530{col 58}{space 4}-5.213255{col 71}{space 3} 2.682878
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.6454525{col 30}{space 2} 2.013569{col 41}{space 1}   -0.32{col 50}{space 3}0.749{col 58}{space 4}-4.591976{col 71}{space 3} 3.301071
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .5391044{col 30}{space 2} 2.013034{col 41}{space 1}    0.27{col 50}{space 3}0.789{col 58}{space 4} -3.40637{col 71}{space 3} 4.484579
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.630525{col 30}{space 2} 2.013064{col 41}{space 1}    0.81{col 50}{space 3}0.418{col 58}{space 4}-2.315008{col 71}{space 3} 5.576057
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.21194293 (.08880731)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. 
. **Interaction: skill and trade openness, Table A13
. 
. gen interaction1=Tradelib*Skill
{txt}(884 missing values generated)

{com}. 
. gllamm Govresp Tradelib Skill interaction1 Private unemployment FDIlib Tertiaryindustry Income employed Female Education Age EqualityP, i(V256) link(ologit) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-2353.1477
{txt}Iteration 1:    log likelihood = {res}-2351.3701
{txt}Iteration 2:    log likelihood = {res}-2348.6195
{txt}Iteration 3:    log likelihood = {res}-2348.0826
{txt}Iteration 4:    log likelihood = {res}-2348.0826


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-2348.0826{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-2348.0826{txt}  
Iteration 2:{col 16}log likelihood = {res}-2348.0396{txt}  
Iteration 3:{col 16}log likelihood = {res}-2348.0389{txt}  
Iteration 4:{col 16}log likelihood = {res}-2348.0389{txt}  
{res} 
{txt}number of level 1 units = {res}1144
{txt}number of level 2 units = {res}24
 
{txt}Condition Number = {res}17595.878
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-2348.0389
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Govresp          {txt}{c |}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0169501{col 30}{space 2} .0063116{col 41}{space 1}    2.69{col 50}{space 3}0.007{col 58}{space 4} .0045796{col 71}{space 3} .0293206
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0483516{col 30}{space 2} .0314063{col 41}{space 1}   -1.54{col 50}{space 3}0.124{col 58}{space 4}-.1099068{col 71}{space 3} .0132035
{txt}{space 4}interaction1 {c |}{col 18}{res}{space 2} .0002859{col 30}{space 2} .0005259{col 41}{space 1}    0.54{col 50}{space 3}0.587{col 58}{space 4}-.0007448{col 71}{space 3} .0013166
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3315223{col 30}{space 2} .1296195{col 41}{space 1}   -2.56{col 50}{space 3}0.011{col 58}{space 4}-.5855717{col 71}{space 3}-.0774728
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5282526{col 30}{space 2}  .216925{col 41}{space 1}    2.44{col 50}{space 3}0.015{col 58}{space 4} .1030874{col 71}{space 3} .9534177
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} .0006187{col 30}{space 2} .0059454{col 41}{space 1}    0.10{col 50}{space 3}0.917{col 58}{space 4} -.011034{col 71}{space 3} .0122715
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0522657{col 30}{space 2} .0132023{col 41}{space 1}   -3.96{col 50}{space 3}0.000{col 58}{space 4}-.0781417{col 71}{space 3}-.0263897
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0415686{col 30}{space 2} .0311981{col 41}{space 1}   -1.33{col 50}{space 3}0.183{col 58}{space 4}-.1027157{col 71}{space 3} .0195785
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0637403{col 30}{space 2} .1209648{col 41}{space 1}   -0.53{col 50}{space 3}0.598{col 58}{space 4}-.3008269{col 71}{space 3} .1733463
{txt}{space 10}Female {c |}{col 18}{res}{space 2} -.108416{col 30}{space 2}  .105987{col 41}{space 1}   -1.02{col 50}{space 3}0.306{col 58}{space 4}-.3161467{col 71}{space 3} .0993147
{txt}{space 7}Education {c |}{col 18}{res}{space 2} .0011692{col 30}{space 2} .0305454{col 41}{space 1}    0.04{col 50}{space 3}0.969{col 58}{space 4}-.0586985{col 71}{space 3}  .061037
{txt}{space 13}Age {c |}{col 18}{res}{space 2}  .001761{col 30}{space 2}  .004773{col 41}{space 1}    0.37{col 50}{space 3}0.712{col 58}{space 4}-.0075939{col 71}{space 3}  .011116
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .3924446{col 30}{space 2} .0243051{col 41}{space 1}   16.15{col 50}{space 3}0.000{col 58}{space 4} .3448075{col 71}{space 3} .4400817
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.749212{col 30}{space 2} 1.125428{col 41}{space 1}   -1.55{col 50}{space 3}0.120{col 58}{space 4}-3.955012{col 71}{space 3}  .456587
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} -.641842{col 30}{space 2} 1.119654{col 41}{space 1}   -0.57{col 50}{space 3}0.566{col 58}{space 4}-2.836324{col 71}{space 3}  1.55264
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .2925384{col 30}{space 2} 1.118664{col 41}{space 1}    0.26{col 50}{space 3}0.794{col 58}{space 4}-1.900003{col 71}{space 3}  2.48508
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .9691257{col 30}{space 2} 1.118968{col 41}{space 1}    0.87{col 50}{space 3}0.386{col 58}{space 4}-1.224011{col 71}{space 3} 3.162262
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}  1.51216{col 30}{space 2} 1.119565{col 41}{space 1}    1.35{col 50}{space 3}0.177{col 58}{space 4}-.6821463{col 71}{space 3} 3.706466
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}  2.01134{col 30}{space 2} 1.120157{col 41}{space 1}    1.80{col 50}{space 3}0.073{col 58}{space 4}-.1841263{col 71}{space 3} 4.206807
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.644156{col 30}{space 2} 1.121142{col 41}{space 1}    2.36{col 50}{space 3}0.018{col 58}{space 4} .4467581{col 71}{space 3} 4.841554
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}  3.76055{col 30}{space 2} 1.123448{col 41}{space 1}    3.35{col 50}{space 3}0.001{col 58}{space 4} 1.558632{col 71}{space 3} 5.962468
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 4.418722{col 30}{space 2} 1.125357{col 41}{space 1}    3.93{col 50}{space 3}0.000{col 58}{space 4} 2.213063{col 71}{space 3} 6.624381
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.01981381 (.02860956)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. gllamm Unemploy Tradelib Skill interaction1 Private unemployment FDIlib Tertiaryindustry Income employed Female Education Age EqualityP, i(V256) link(ologit) adapt
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-1840.4798
{txt}Iteration 1:    log likelihood = {res}-1838.7542
{txt}Iteration 2:    log likelihood = {res}-1838.6428
{txt}Iteration 3:    log likelihood = {res}-1838.5844
{txt}Iteration 4:    log likelihood = {res}-1838.5658
{txt}Iteration 5:    log likelihood = {res}-1838.5392
{txt}Iteration 6:    log likelihood = {res} -1838.534
{txt}Iteration 7:    log likelihood = {res}-1838.5185
{txt}Iteration 8:    log likelihood = {res}-1838.5167


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-1838.5167{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-1838.5166{txt}  
Iteration 2:{col 16}log likelihood = {res}-1838.4961{txt}  
Iteration 3:{col 16}log likelihood = {res} -1838.496{txt}  
{res} 
{txt}number of level 1 units = {res}1071
{txt}number of level 2 units = {res}23
 
{txt}Condition Number = {res}19991.313
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-1838.496
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Unemploy         {txt}{c |}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0206979{col 30}{space 2} .0109045{col 41}{space 1}    1.90{col 50}{space 3}0.058{col 58}{space 4}-.0006746{col 71}{space 3} .0420704
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0198507{col 30}{space 2}  .031985{col 41}{space 1}    0.62{col 50}{space 3}0.535{col 58}{space 4}-.0428388{col 71}{space 3} .0825401
{txt}{space 4}interaction1 {c |}{col 18}{res}{space 2}   .00028{col 30}{space 2} .0005388{col 41}{space 1}    0.52{col 50}{space 3}0.603{col 58}{space 4}-.0007761{col 71}{space 3}  .001336
{txt}{space 9}Private {c |}{col 18}{res}{space 2} -.201349{col 30}{space 2} .1401416{col 41}{space 1}   -1.44{col 50}{space 3}0.151{col 58}{space 4}-.4760214{col 71}{space 3} .0733235
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5467179{col 30}{space 2} .3959722{col 41}{space 1}    1.38{col 50}{space 3}0.167{col 58}{space 4}-.2293734{col 71}{space 3} 1.322809
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2}-.0171559{col 30}{space 2} .0114538{col 41}{space 1}   -1.50{col 50}{space 3}0.134{col 58}{space 4} -.039605{col 71}{space 3} .0052932
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0256531{col 30}{space 2} .0249702{col 41}{space 1}   -1.03{col 50}{space 3}0.304{col 58}{space 4}-.0745938{col 71}{space 3} .0232876
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0762236{col 30}{space 2} .0343629{col 41}{space 1}   -2.22{col 50}{space 3}0.027{col 58}{space 4}-.1435737{col 71}{space 3}-.0088735
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.0646506{col 30}{space 2} .1311837{col 41}{space 1}   -0.49{col 50}{space 3}0.622{col 58}{space 4} -.321766{col 71}{space 3} .1924648
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1978227{col 30}{space 2} .1135626{col 41}{space 1}   -1.74{col 50}{space 3}0.082{col 58}{space 4}-.4204014{col 71}{space 3} .0247559
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0443083{col 30}{space 2} .0330235{col 41}{space 1}   -1.34{col 50}{space 3}0.180{col 58}{space 4}-.1090333{col 71}{space 3} .0204166
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0044422{col 30}{space 2}  .005072{col 41}{space 1}   -0.88{col 50}{space 3}0.381{col 58}{space 4}-.0143831{col 71}{space 3} .0054987
{txt}{space 7}EqualityP {c |}{col 18}{res}{space 2} .0771365{col 30}{space 2} .0219986{col 41}{space 1}    3.51{col 50}{space 3}0.000{col 58}{space 4} .0340201{col 71}{space 3} .1202529
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-3.549652{col 30}{space 2}  1.99471{col 41}{space 1}   -1.78{col 50}{space 3}0.075{col 58}{space 4} -7.45921{col 71}{space 3} .3599072
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.928394{col 30}{space 2}  1.98937{col 41}{space 1}   -1.47{col 50}{space 3}0.141{col 58}{space 4}-6.827487{col 71}{space 3} .9706984
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-2.309907{col 30}{space 2}  1.98689{col 41}{space 1}   -1.16{col 50}{space 3}0.245{col 58}{space 4}-6.204139{col 71}{space 3} 1.584326
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} -2.03829{col 30}{space 2} 1.986227{col 41}{space 1}   -1.03{col 50}{space 3}0.305{col 58}{space 4}-5.931224{col 71}{space 3} 1.854644
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.724108{col 30}{space 2} 1.985495{col 41}{space 1}   -0.87{col 50}{space 3}0.385{col 58}{space 4}-5.615606{col 71}{space 3}  2.16739
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.334647{col 30}{space 2} 1.984603{col 41}{space 1}   -0.67{col 50}{space 3}0.501{col 58}{space 4}-5.224398{col 71}{space 3} 2.555103
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.7150927{col 30}{space 2} 1.983721{col 41}{space 1}   -0.36{col 50}{space 3}0.718{col 58}{space 4}-4.603114{col 71}{space 3} 3.172928
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .4694299{col 30}{space 2} 1.983098{col 41}{space 1}    0.24{col 50}{space 3}0.813{col 58}{space 4}-3.417372{col 71}{space 3} 4.356231
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}  1.56116{col 30}{space 2}   1.9833{col 41}{space 1}    0.79{col 50}{space 3}0.431{col 58}{space 4}-2.326036{col 71}{space 3} 5.448355
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.21736442 (.09037967)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. **Replace perceived inequality with province-level Gini, Table A14
. 
. gllamm Govresp Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age GINI, i(V256) link(ologit) adapt
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-2097.5704
{txt}Iteration 1:    log likelihood = {res}-2095.4671
{txt}Iteration 2:    log likelihood = {res}-2095.4667


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-2095.4667{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-2095.4667{txt}  
Iteration 2:{col 16}log likelihood = {res} -2095.341{txt}  
Iteration 3:{col 16}log likelihood = {res}-2095.3398{txt}  
Iteration 4:{col 16}log likelihood = {res}-2095.3398{txt}  
{res} 
{txt}number of level 1 units = {res}967
{txt}number of level 2 units = {res}21
 
{txt}Condition Number = {res}5783.153
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-2095.3398
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         Govresp{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Govresp          {txt}{c |}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0193842{col 30}{space 2} .0109185{col 41}{space 1}    1.78{col 50}{space 3}0.076{col 58}{space 4}-.0020156{col 71}{space 3}  .040784
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.3414734{col 30}{space 2} .1425675{col 41}{space 1}   -2.40{col 50}{space 3}0.017{col 58}{space 4}-.6209006{col 71}{space 3}-.0620463
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .5544137{col 30}{space 2} .4168394{col 41}{space 1}    1.33{col 50}{space 3}0.184{col 58}{space 4}-.2625765{col 71}{space 3} 1.371404
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} .0070569{col 30}{space 2}  .011314{col 41}{space 1}    0.62{col 50}{space 3}0.533{col 58}{space 4}-.0151181{col 71}{space 3} .0292319
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0426298{col 30}{space 2} .0261883{col 41}{space 1}   -1.63{col 50}{space 3}0.104{col 58}{space 4}-.0939579{col 71}{space 3} .0086983
{txt}{space 10}Income {c |}{col 18}{res}{space 2} -.041397{col 30}{space 2} .0351468{col 41}{space 1}   -1.18{col 50}{space 3}0.239{col 58}{space 4}-.1102833{col 71}{space 3} .0274894
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.1450316{col 30}{space 2} .1369475{col 41}{space 1}   -1.06{col 50}{space 3}0.290{col 58}{space 4}-.4134437{col 71}{space 3} .1233806
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.0523305{col 30}{space 2} .1158038{col 41}{space 1}   -0.45{col 50}{space 3}0.651{col 58}{space 4}-.2793019{col 71}{space 3} .1746408
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0359818{col 30}{space 2}  .033267{col 41}{space 1}   -1.08{col 50}{space 3}0.279{col 58}{space 4}-.1011839{col 71}{space 3} .0292203
{txt}{space 11}Skill {c |}{col 18}{res}{space 2}-.0654742{col 30}{space 2} .0279266{col 41}{space 1}   -2.34{col 50}{space 3}0.019{col 58}{space 4}-.1202094{col 71}{space 3} -.010739
{txt}{space 13}Age {c |}{col 18}{res}{space 2} .0072342{col 30}{space 2} .0053004{col 41}{space 1}    1.36{col 50}{space 3}0.172{col 58}{space 4}-.0031545{col 71}{space 3} .0176229
{txt}{space 12}GINI {c |}{col 18}{res}{space 2} 3.604688{col 30}{space 2} 3.532124{col 41}{space 1}    1.02{col 50}{space 3}0.307{col 58}{space 4}-3.318148{col 71}{space 3} 10.52752
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-1.726332{col 30}{space 2} 2.849358{col 41}{space 1}   -0.61{col 50}{space 3}0.545{col 58}{space 4} -7.31097{col 71}{space 3} 3.858307
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-.7115826{col 30}{space 2} 2.845987{col 41}{space 1}   -0.25{col 50}{space 3}0.803{col 58}{space 4}-6.289615{col 71}{space 3}  4.86645
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .1405274{col 30}{space 2} 2.845884{col 41}{space 1}    0.05{col 50}{space 3}0.961{col 58}{space 4}-5.437302{col 71}{space 3} 5.718357
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} .7677712{col 30}{space 2} 2.846973{col 41}{space 1}    0.27{col 50}{space 3}0.787{col 58}{space 4}-4.812193{col 71}{space 3} 6.347736
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.191281{col 30}{space 2} 2.847477{col 41}{space 1}    0.42{col 50}{space 3}0.676{col 58}{space 4}-4.389672{col 71}{space 3} 6.772233
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 1.592246{col 30}{space 2} 2.847793{col 41}{space 1}    0.56{col 50}{space 3}0.576{col 58}{space 4}-3.989327{col 71}{space 3} 7.173818
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 2.127341{col 30}{space 2} 2.848226{col 41}{space 1}    0.75{col 50}{space 3}0.455{col 58}{space 4}-3.455079{col 71}{space 3} 7.709762
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 3.122114{col 30}{space 2} 2.849027{col 41}{space 1}    1.10{col 50}{space 3}0.273{col 58}{space 4}-2.461875{col 71}{space 3} 8.706104
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2} 3.725931{col 30}{space 2} 2.849709{col 41}{space 1}    1.31{col 50}{space 3}0.191{col 58}{space 4}-1.859396{col 71}{space 3} 9.311257
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.23120478 (.11096648)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
. gllamm Unemploy Tradelib Private unemployment FDIlib Tertiaryindustry Income employed Female Education Skill Age GINI, i(V256) link(ologit) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-1607.8476
{txt}Iteration 1:    log likelihood = {res}-1605.9598
{txt}Iteration 2:    log likelihood = {res}-1605.7508
{txt}Iteration 3:    log likelihood = {res}-1605.6711
{txt}Iteration 4:    log likelihood = {res}-1605.6248
{txt}Iteration 5:    log likelihood = {res}-1605.6235


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-1605.6235{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-1605.6106{txt}  
Iteration 2:{col 16}log likelihood = {res}-1605.4996{txt}  
Iteration 3:{col 16}log likelihood = {res}-1605.4989{txt}  
Iteration 4:{col 16}log likelihood = {res}-1605.4989{txt}  
{res} 
{txt}number of level 1 units = {res}940
{txt}number of level 2 units = {res}21
 
{txt}Condition Number = {res}6042.8099
 
{txt}gllamm model 
{res} 
{txt}log likelihood = {res}-1605.4989
 
{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Unemploy{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Unemploy         {txt}{c |}
{space 8}Tradelib {c |}{col 18}{res}{space 2} .0208093{col 30}{space 2} .0113505{col 41}{space 1}    1.83{col 50}{space 3}0.067{col 58}{space 4}-.0014372{col 71}{space 3} .0430559
{txt}{space 9}Private {c |}{col 18}{res}{space 2}-.1455053{col 30}{space 2} .1479718{col 41}{space 1}   -0.98{col 50}{space 3}0.325{col 58}{space 4}-.4355248{col 71}{space 3} .1445141
{txt}{space 4}unemployment {c |}{col 18}{res}{space 2} .1341752{col 30}{space 2} .4133979{col 41}{space 1}    0.32{col 50}{space 3}0.746{col 58}{space 4}-.6760697{col 71}{space 3} .9444201
{txt}{space 10}FDIlib {c |}{col 18}{res}{space 2} -.024181{col 30}{space 2} .0107594{col 41}{space 1}   -2.25{col 50}{space 3}0.025{col 58}{space 4}-.0452692{col 71}{space 3}-.0030929
{txt}Tertiaryindustry {c |}{col 18}{res}{space 2}-.0507968{col 30}{space 2}  .026026{col 41}{space 1}   -1.95{col 50}{space 3}0.051{col 58}{space 4}-.1018067{col 71}{space 3} .0002132
{txt}{space 10}Income {c |}{col 18}{res}{space 2}-.0619073{col 30}{space 2} .0376816{col 41}{space 1}   -1.64{col 50}{space 3}0.100{col 58}{space 4}-.1357618{col 71}{space 3} .0119473
{txt}{space 8}employed {c |}{col 18}{res}{space 2}-.1226819{col 30}{space 2} .1419206{col 41}{space 1}   -0.86{col 50}{space 3}0.387{col 58}{space 4}-.4008412{col 71}{space 3} .1554773
{txt}{space 10}Female {c |}{col 18}{res}{space 2}-.1627278{col 30}{space 2} .1206851{col 41}{space 1}   -1.35{col 50}{space 3}0.178{col 58}{space 4}-.3992662{col 71}{space 3} .0738106
{txt}{space 7}Education {c |}{col 18}{res}{space 2}-.0566575{col 30}{space 2} .0347833{col 41}{space 1}   -1.63{col 50}{space 3}0.103{col 58}{space 4}-.1248315{col 71}{space 3} .0115165
{txt}{space 11}Skill {c |}{col 18}{res}{space 2} .0292888{col 30}{space 2} .0275327{col 41}{space 1}    1.06{col 50}{space 3}0.287{col 58}{space 4}-.0246743{col 71}{space 3} .0832519
{txt}{space 13}Age {c |}{col 18}{res}{space 2}-.0029321{col 30}{space 2} .0054335{col 41}{space 1}   -0.54{col 50}{space 3}0.589{col 58}{space 4}-.0135815{col 71}{space 3} .0077173
{txt}{space 12}GINI {c |}{col 18}{res}{space 2}-5.130994{col 30}{space 2} 3.646088{col 41}{space 1}   -1.41{col 50}{space 3}0.159{col 58}{space 4} -12.2772{col 71}{space 3} 2.015208
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-8.645004{col 30}{space 2} 2.826505{col 41}{space 1}   -3.06{col 50}{space 3}0.002{col 58}{space 4}-14.18485{col 71}{space 3}-3.105156
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-7.946253{col 30}{space 2} 2.820908{col 41}{space 1}   -2.82{col 50}{space 3}0.005{col 58}{space 4}-13.47513{col 71}{space 3}-2.417376
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-7.363406{col 30}{space 2} 2.817851{col 41}{space 1}   -2.61{col 50}{space 3}0.009{col 58}{space 4}-12.88629{col 71}{space 3} -1.84052
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut14           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-7.078077{col 30}{space 2} 2.816698{col 41}{space 1}   -2.51{col 50}{space 3}0.012{col 58}{space 4} -12.5987{col 71}{space 3}-1.557451
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut15           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-6.820913{col 30}{space 2} 2.815799{col 41}{space 1}   -2.42{col 50}{space 3}0.015{col 58}{space 4}-12.33978{col 71}{space 3}-1.302048
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut16           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-6.426784{col 30}{space 2} 2.814581{col 41}{space 1}   -2.28{col 50}{space 3}0.022{col 58}{space 4}-11.94326{col 71}{space 3}-.9103069
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut17           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-5.811508{col 30}{space 2} 2.813251{col 41}{space 1}   -2.07{col 50}{space 3}0.039{col 58}{space 4}-11.32538{col 71}{space 3}-.2976379
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut18           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-4.618046{col 30}{space 2} 2.810835{col 41}{space 1}   -1.64{col 50}{space 3}0.100{col 58}{space 4}-10.12718{col 71}{space 3} .8910885
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut19           {txt}{c |}
{space 11}_cons {c |}{col 18}{res}{space 2}-3.448604{col 30}{space 2} 2.808748{col 41}{space 1}   -1.23{col 50}{space 3}0.220{col 58}{space 4}-8.953648{col 71}{space 3}  2.05644
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}V256{txt})
{res} 
{txt}    var(1): {res}.24693031 (.10089997)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
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  {txt}log type:  {res}smcl
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